{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import linecache\n",
    "import datetime\n",
    "import sys\n",
    "import numpy as np\n",
    "import random\n",
    "from scipy.sparse import *\n",
    "from scipy import *\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import os.path\n",
    "import matplotlib\n",
    "import copy\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from tempfile import TemporaryFile\n",
    "import pickle\n",
    "def flushPrint(variable):\n",
    "    sys.stdout.write('\\r')\n",
    "    sys.stdout.write('%s' % variable)\n",
    "    sys.stdout.flush()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取2016年的流量数据并与2020最新部分比例数据合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pi</th>\n",
       "      <th>pj</th>\n",
       "      <th>loni</th>\n",
       "      <th>lati</th>\n",
       "      <th>cityi.id</th>\n",
       "      <th>provi.id</th>\n",
       "      <th>lonj</th>\n",
       "      <th>latj</th>\n",
       "      <th>cityj.id</th>\n",
       "      <th>provj.id</th>\n",
       "      <th>flowij</th>\n",
       "      <th>distij</th>\n",
       "      <th>sameprov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1220637</td>\n",
       "      <td>1220637</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>1082923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1220637</td>\n",
       "      <td>488412</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>117.344841</td>\n",
       "      <td>39.284732</td>\n",
       "      <td>120000</td>\n",
       "      <td>120000</td>\n",
       "      <td>4642</td>\n",
       "      <td>128.019884</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1220637</td>\n",
       "      <td>371707</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>114.439494</td>\n",
       "      <td>38.130579</td>\n",
       "      <td>130100</td>\n",
       "      <td>130000</td>\n",
       "      <td>2962</td>\n",
       "      <td>284.863566</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1220637</td>\n",
       "      <td>188880</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>118.335459</td>\n",
       "      <td>39.713888</td>\n",
       "      <td>130200</td>\n",
       "      <td>130000</td>\n",
       "      <td>1632</td>\n",
       "      <td>172.096010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1220637</td>\n",
       "      <td>82307</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>119.187239</td>\n",
       "      <td>40.085314</td>\n",
       "      <td>130300</td>\n",
       "      <td>130000</td>\n",
       "      <td>1021</td>\n",
       "      <td>236.130974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1220637</td>\n",
       "      <td>214644</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>114.542925</td>\n",
       "      <td>36.552456</td>\n",
       "      <td>130400</td>\n",
       "      <td>130000</td>\n",
       "      <td>3332</td>\n",
       "      <td>435.600363</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1220637</td>\n",
       "      <td>138234</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>114.816896</td>\n",
       "      <td>37.212320</td>\n",
       "      <td>130500</td>\n",
       "      <td>130000</td>\n",
       "      <td>1873</td>\n",
       "      <td>358.424618</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1220637</td>\n",
       "      <td>293124</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>115.171130</td>\n",
       "      <td>39.021666</td>\n",
       "      <td>130600</td>\n",
       "      <td>130000</td>\n",
       "      <td>6091</td>\n",
       "      <td>167.515139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1220637</td>\n",
       "      <td>87130</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>115.031804</td>\n",
       "      <td>40.864962</td>\n",
       "      <td>130700</td>\n",
       "      <td>130000</td>\n",
       "      <td>2891</td>\n",
       "      <td>139.016370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1220637</td>\n",
       "      <td>61064</td>\n",
       "      <td>116.412574</td>\n",
       "      <td>40.185609</td>\n",
       "      <td>110000</td>\n",
       "      <td>110000</td>\n",
       "      <td>117.547029</td>\n",
       "      <td>41.347208</td>\n",
       "      <td>130800</td>\n",
       "      <td>130000</td>\n",
       "      <td>1866</td>\n",
       "      <td>160.655042</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pi       pj        loni       lati  cityi.id  provi.id        lonj  \\\n",
       "0  1220637  1220637  116.412574  40.185609    110000    110000  116.412574   \n",
       "1  1220637   488412  116.412574  40.185609    110000    110000  117.344841   \n",
       "2  1220637   371707  116.412574  40.185609    110000    110000  114.439494   \n",
       "3  1220637   188880  116.412574  40.185609    110000    110000  118.335459   \n",
       "4  1220637    82307  116.412574  40.185609    110000    110000  119.187239   \n",
       "5  1220637   214644  116.412574  40.185609    110000    110000  114.542925   \n",
       "6  1220637   138234  116.412574  40.185609    110000    110000  114.816896   \n",
       "7  1220637   293124  116.412574  40.185609    110000    110000  115.171130   \n",
       "8  1220637    87130  116.412574  40.185609    110000    110000  115.031804   \n",
       "9  1220637    61064  116.412574  40.185609    110000    110000  117.547029   \n",
       "\n",
       "        latj  cityj.id  provj.id   flowij      distij  sameprov  \n",
       "0  40.185609    110000    110000  1082923    0.000000         1  \n",
       "1  39.284732    120000    120000     4642  128.019884         0  \n",
       "2  38.130579    130100    130000     2962  284.863566         0  \n",
       "3  39.713888    130200    130000     1632  172.096010         0  \n",
       "4  40.085314    130300    130000     1021  236.130974         0  \n",
       "5  36.552456    130400    130000     3332  435.600363         0  \n",
       "6  37.212320    130500    130000     1873  358.424618         0  \n",
       "7  39.021666    130600    130000     6091  167.515139         0  \n",
       "8  40.864962    130700    130000     2891  139.016370         0  \n",
       "9  41.347208    130800    130000     1866  160.655042         0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取工作簿和工作簿中的工作表\n",
    "df=pd.read_csv('city_flow_v1.csv')\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.995342957403680654"
     ]
    }
   ],
   "source": [
    "cityi = list(df['cityi.id'])\n",
    "cityj = list(df['cityj.id'])\n",
    "all_cities = set(cityi+cityj)\n",
    "cities_temp = {}\n",
    "flows = {}\n",
    "for n,i in enumerate(df.iterrows()):\n",
    "    if n % 1000 == 0:\n",
    "        flushPrint(n/len(df))\n",
    "    cityi=long(i[1]['cityi.id'])\n",
    "    cityj=long(i[1]['cityj.id'])\n",
    "    provi=long(i[1]['provi.id'])\n",
    "    provj=long(i[1]['provj.id'])\n",
    "    cities_temp[cityi] = {'id':cityi,'lon':i[1]['loni'],'lat':i[1]['lati'],'provid':provi,'pop':i[1]['pi']}\n",
    "    cities_temp[cityj] = {'id':cityj,'lon':i[1]['lonj'],'lat':i[1]['latj'],'provid':provj,'pop':i[1]['pj']}\n",
    "    value = flows.get((cityi,cityj),0)\n",
    "    flows[(cityi,cityj)] = value + i[1]['flowij']\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#存储到流量矩阵matrix\n",
    "nodes_temp={} #cityid: row index mapping\n",
    "matrix = np.zeros([len(cities_temp), len(cities_temp)])\n",
    "self_flux = np.zeros(len(cities_temp))\n",
    "pij1 = np.zeros([len(cities_temp), len(cities_temp)])\n",
    "for key,value in flows.items():\n",
    "    id1 = nodes_temp.get(key[0],-1)\n",
    "    if id1<0:\n",
    "        id1 = len(nodes_temp)\n",
    "        nodes_temp[key[0]] = len(nodes_temp)\n",
    "    id2 = nodes_temp.get(key[1],-1)\n",
    "    if id2<0:\n",
    "        id2 = len(nodes_temp)\n",
    "        nodes_temp[key[1]] = len(nodes_temp)\n",
    "    matrix[id1, id2] = value\n",
    "for i in range(matrix.shape[0]):\n",
    "    self_flux[i] = matrix[i, i]\n",
    "    matrix[i, i] = 0\n",
    "    if np.sum(matrix[i,:])>0:\n",
    "        pij1[i,:]=matrix[i,:]/np.sum(matrix[i,:])\n",
    "#根据我国人口总数，核算每个城市的人口数以及总流动人口占比\n",
    "all_pop = np.sum(np.array([v['pop'] for k,v in cities_temp.items()]))\n",
    "china_pop = 13*1e8\n",
    "frac = china_pop / all_pop\n",
    "for k,v in cities_temp.items():\n",
    "    cities_temp[k]['pop'] = frac * v['pop']\n",
    "flowing_ratio=np.sum(matrix) / all_pop\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读取每个城市id与name的映射"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNTY_ID</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>CITY_ID</th>\n",
       "      <th>CITY</th>\n",
       "      <th>PROV_ID</th>\n",
       "      <th>PROV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110101</td>\n",
       "      <td>东城区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110102</td>\n",
       "      <td>西城区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110105</td>\n",
       "      <td>朝阳区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110106</td>\n",
       "      <td>丰台区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110107</td>\n",
       "      <td>石景山区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110108</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110109</td>\n",
       "      <td>门头沟区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110111</td>\n",
       "      <td>房山区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110112</td>\n",
       "      <td>通州区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>110113</td>\n",
       "      <td>顺义区</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNTY_ID COUNTY  CITY_ID CITY  PROV_ID PROV\n",
       "0     110101    东城区   110000  北京市   110000  北京市\n",
       "1     110102    西城区   110000  北京市   110000  北京市\n",
       "2     110105    朝阳区   110000  北京市   110000  北京市\n",
       "3     110106    丰台区   110000  北京市   110000  北京市\n",
       "4     110107   石景山区   110000  北京市   110000  北京市\n",
       "5     110108    海淀区   110000  北京市   110000  北京市\n",
       "6     110109   门头沟区   110000  北京市   110000  北京市\n",
       "7     110111    房山区   110000  北京市   110000  北京市\n",
       "8     110112    通州区   110000  北京市   110000  北京市\n",
       "9     110113    顺义区   110000  北京市   110000  北京市"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('county_city_province.csv')\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_to_city={}\n",
    "for i in df.iterrows():\n",
    "    id_to_city[i[1]['CITY_ID']]={'city':i[1]['CITY'],'prov':i[1]['PROV']}\n",
    "city_properties = {}\n",
    "nodes = {}\n",
    "for k,v in cities_temp.items():\n",
    "    new_property = id_to_city.get(k, {'city':str(k), 'prov':str(k)})\n",
    "    v['prov'] = new_property['prov']\n",
    "    city_properties[new_property['city']] = v\n",
    "    row_idx = nodes_temp.get(v['id'],-1)\n",
    "    if row_idx >= 0:\n",
    "        nodes[new_property['city']] = row_idx\n",
    "    else:\n",
    "        print(new_property['city'])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'北京市': 0,\n",
       " '天津市': 1,\n",
       " '石家庄市': 2,\n",
       " '唐山市': 3,\n",
       " '秦皇岛市': 4,\n",
       " '邯郸市': 5,\n",
       " '邢台市': 6,\n",
       " '保定市': 7,\n",
       " '张家口市': 8,\n",
       " '承德市': 9,\n",
       " '沧州市': 10,\n",
       " '廊坊市': 11,\n",
       " '衡水市': 12,\n",
       " '太原市': 13,\n",
       " '大同市': 14,\n",
       " '阳泉市': 15,\n",
       " '长治市': 16,\n",
       " '晋城市': 17,\n",
       " '朔州市': 18,\n",
       " '晋中市': 19,\n",
       " '运城市': 20,\n",
       " '忻州市': 21,\n",
       " '临汾市': 22,\n",
       " '吕梁市': 23,\n",
       " '呼和浩特市': 24,\n",
       " '包头市': 25,\n",
       " '乌海市': 26,\n",
       " '赤峰市': 27,\n",
       " '通辽市': 28,\n",
       " '鄂尔多斯市': 29,\n",
       " '呼伦贝尔市': 30,\n",
       " '巴彦淖尔市': 31,\n",
       " '乌兰察布市': 32,\n",
       " '兴安盟': 33,\n",
       " '锡林郭勒盟': 34,\n",
       " '阿拉善盟': 35,\n",
       " '沈阳市': 36,\n",
       " '大连市': 37,\n",
       " '鞍山市': 38,\n",
       " '抚顺市': 39,\n",
       " '本溪市': 40,\n",
       " '丹东市': 41,\n",
       " '锦州市': 42,\n",
       " '营口市': 43,\n",
       " '阜新市': 44,\n",
       " '辽阳市': 45,\n",
       " '盘锦市': 46,\n",
       " '铁岭市': 47,\n",
       " '朝阳市': 48,\n",
       " '葫芦岛市': 49,\n",
       " '长春市': 50,\n",
       " '吉林市': 51,\n",
       " '四平市': 52,\n",
       " '辽源市': 53,\n",
       " '通化市': 54,\n",
       " '白山市': 55,\n",
       " '松原市': 56,\n",
       " '白城市': 57,\n",
       " '延边朝鲜族自治州': 58,\n",
       " '哈尔滨市': 59,\n",
       " '齐齐哈尔市': 60,\n",
       " '鸡西市': 61,\n",
       " '鹤岗市': 62,\n",
       " '双鸭山市': 63,\n",
       " '大庆市': 64,\n",
       " '伊春市': 65,\n",
       " '佳木斯市': 66,\n",
       " '七台河市': 67,\n",
       " '牡丹江市': 68,\n",
       " '黑河市': 69,\n",
       " '绥化市': 70,\n",
       " '大兴安岭地区': 71,\n",
       " '上海市': 72,\n",
       " '南京市': 73,\n",
       " '无锡市': 74,\n",
       " '徐州市': 75,\n",
       " '常州市': 76,\n",
       " '苏州市': 77,\n",
       " '南通市': 78,\n",
       " '连云港市': 79,\n",
       " '淮安市': 80,\n",
       " '盐城市': 81,\n",
       " '扬州市': 82,\n",
       " '镇江市': 83,\n",
       " '泰州市': 84,\n",
       " '宿迁市': 85,\n",
       " '杭州市': 86,\n",
       " '宁波市': 87,\n",
       " '温州市': 88,\n",
       " '嘉兴市': 89,\n",
       " '湖州市': 90,\n",
       " '绍兴市': 91,\n",
       " '金华市': 92,\n",
       " '衢州市': 93,\n",
       " '舟山市': 94,\n",
       " '台州市': 95,\n",
       " '丽水市': 96,\n",
       " '合肥市': 97,\n",
       " '芜湖市': 98,\n",
       " '蚌埠市': 99,\n",
       " '淮南市': 100,\n",
       " '马鞍山市': 101,\n",
       " '淮北市': 102,\n",
       " '铜陵市': 103,\n",
       " '安庆市': 104,\n",
       " '黄山市': 105,\n",
       " '滁州市': 106,\n",
       " '阜阳市': 107,\n",
       " '341300': 108,\n",
       " '六安市': 109,\n",
       " '亳州市': 110,\n",
       " '池州市': 111,\n",
       " '宣城市': 112,\n",
       " '福州市': 113,\n",
       " '厦门市': 114,\n",
       " '莆田市': 115,\n",
       " '三明市': 116,\n",
       " '泉州市': 117,\n",
       " '漳州市': 118,\n",
       " '南平市': 119,\n",
       " '龙岩市': 120,\n",
       " '宁德市': 121,\n",
       " '南昌市': 122,\n",
       " '景德镇市': 123,\n",
       " '萍乡市': 124,\n",
       " '九江市': 125,\n",
       " '新余市': 126,\n",
       " '鹰潭市': 127,\n",
       " '赣州市': 128,\n",
       " '吉安市': 129,\n",
       " '宜春市': 130,\n",
       " '抚州市': 131,\n",
       " '上饶市': 132,\n",
       " '济南市': 133,\n",
       " '青岛市': 134,\n",
       " '淄博市': 135,\n",
       " '枣庄市': 136,\n",
       " '东营市': 137,\n",
       " '烟台市': 138,\n",
       " '潍坊市': 139,\n",
       " '济宁市': 140,\n",
       " '泰安市': 141,\n",
       " '威海市': 142,\n",
       " '日照市': 143,\n",
       " '莱芜市': 144,\n",
       " '临沂市': 145,\n",
       " '德州市': 146,\n",
       " '聊城市': 147,\n",
       " '滨州市': 148,\n",
       " '菏泽市': 149,\n",
       " '郑州市': 150,\n",
       " '开封市': 151,\n",
       " '洛阳市': 152,\n",
       " '平顶山市': 153,\n",
       " '安阳市': 154,\n",
       " '鹤壁市': 155,\n",
       " '新乡市': 156,\n",
       " '焦作市': 157,\n",
       " '濮阳市': 158,\n",
       " '许昌市': 159,\n",
       " '漯河市': 160,\n",
       " '三门峡市': 161,\n",
       " '南阳市': 162,\n",
       " '商丘市': 163,\n",
       " '信阳市': 164,\n",
       " '周口市': 165,\n",
       " '驻马店市': 166,\n",
       " '济源市': 167,\n",
       " '武汉市': 168,\n",
       " '黄石市': 169,\n",
       " '十堰市': 170,\n",
       " '宜昌市': 171,\n",
       " '襄阳市': 172,\n",
       " '鄂州市': 173,\n",
       " '荆门市': 174,\n",
       " '孝感市': 175,\n",
       " '荆州市': 176,\n",
       " '黄冈市': 177,\n",
       " '咸宁市': 178,\n",
       " '随州市': 179,\n",
       " '恩施土家族苗族自治州': 180,\n",
       " '仙桃市': 181,\n",
       " '潜江市': 182,\n",
       " '天门市': 183,\n",
       " '神农架林区': 184,\n",
       " '长沙市': 185,\n",
       " '株洲市': 186,\n",
       " '湘潭市': 187,\n",
       " '衡阳市': 188,\n",
       " '邵阳市': 189,\n",
       " '岳阳市': 190,\n",
       " '常德市': 191,\n",
       " '张家界市': 192,\n",
       " '益阳市': 193,\n",
       " '郴州市': 194,\n",
       " '永州市': 195,\n",
       " '怀化市': 196,\n",
       " '娄底市': 197,\n",
       " '湘西土家族苗族自治州': 198,\n",
       " '广州市': 199,\n",
       " '韶关市': 200,\n",
       " '深圳市': 201,\n",
       " '珠海市': 202,\n",
       " '汕头市': 203,\n",
       " '佛山市': 204,\n",
       " '江门市': 205,\n",
       " '湛江市': 206,\n",
       " '茂名市': 207,\n",
       " '肇庆市': 208,\n",
       " '惠州市': 209,\n",
       " '梅州市': 210,\n",
       " '汕尾市': 211,\n",
       " '河源市': 212,\n",
       " '阳江市': 213,\n",
       " '清远市': 214,\n",
       " '东莞市': 215,\n",
       " '中山市': 216,\n",
       " '东沙群岛': 217,\n",
       " '潮州市': 218,\n",
       " '揭阳市': 219,\n",
       " '云浮市': 220,\n",
       " '南宁市': 221,\n",
       " '柳州市': 222,\n",
       " '桂林市': 223,\n",
       " '梧州市': 224,\n",
       " '北海市': 225,\n",
       " '防城港市': 226,\n",
       " '钦州市': 227,\n",
       " '贵港市': 228,\n",
       " '玉林市': 229,\n",
       " '百色市': 230,\n",
       " '贺州市': 231,\n",
       " '河池市': 232,\n",
       " '来宾市': 233,\n",
       " '崇左市': 234,\n",
       " '海口市': 235,\n",
       " '三亚市': 236,\n",
       " '三沙市': 237,\n",
       " '儋州市': 238,\n",
       " '五指山市': 239,\n",
       " '琼海市': 240,\n",
       " '文昌市': 241,\n",
       " '万宁市': 242,\n",
       " '东方市': 243,\n",
       " '定安县': 244,\n",
       " '屯昌县': 245,\n",
       " '澄迈县': 246,\n",
       " '临高县': 247,\n",
       " '白沙黎族自治县': 248,\n",
       " '昌江黎族自治县': 249,\n",
       " '乐东黎族自治县': 250,\n",
       " '陵水黎族自治县': 251,\n",
       " '保亭黎族苗族自治县': 252,\n",
       " '琼中黎族苗族自治县': 253,\n",
       " '重庆市': 254,\n",
       " '成都市': 255,\n",
       " '自贡市': 256,\n",
       " '攀枝花市': 257,\n",
       " '泸州市': 258,\n",
       " '德阳市': 259,\n",
       " '绵阳市': 260,\n",
       " '广元市': 261,\n",
       " '遂宁市': 262,\n",
       " '内江市': 263,\n",
       " '乐山市': 264,\n",
       " '南充市': 265,\n",
       " '眉山市': 266,\n",
       " '宜宾市': 267,\n",
       " '广安市': 268,\n",
       " '达州市': 269,\n",
       " '雅安市': 270,\n",
       " '巴中市': 271,\n",
       " '资阳市': 272,\n",
       " '阿坝藏族羌族自治州': 273,\n",
       " '甘孜藏族自治州': 274,\n",
       " '凉山彝族自治州': 275,\n",
       " '贵阳市': 276,\n",
       " '六盘水市': 277,\n",
       " '遵义市': 278,\n",
       " '安顺市': 279,\n",
       " '毕节市': 280,\n",
       " '铜仁市': 281,\n",
       " '黔西南布依族苗族自治州': 282,\n",
       " '黔东南苗族侗族自治州': 283,\n",
       " '黔南布依族苗族自治州': 284,\n",
       " '昆明市': 285,\n",
       " '曲靖市': 286,\n",
       " '玉溪市': 287,\n",
       " '保山市': 288,\n",
       " '昭通市': 289,\n",
       " '丽江市': 290,\n",
       " '普洱市': 291,\n",
       " '临沧市': 292,\n",
       " '楚雄彝族自治州': 293,\n",
       " '红河哈尼族彝族自治州': 294,\n",
       " '文山壮族苗族自治州': 295,\n",
       " '西双版纳傣族自治州': 296,\n",
       " '大理白族自治州': 297,\n",
       " '德宏傣族景颇族自治州': 298,\n",
       " '怒江傈僳族自治州': 299,\n",
       " '迪庆藏族自治州': 300,\n",
       " '拉萨市': 301,\n",
       " '日喀则市': 302,\n",
       " '昌都市': 303,\n",
       " '林芝市': 304,\n",
       " '山南市': 305,\n",
       " '那曲地区': 306,\n",
       " '阿里地区': 307,\n",
       " '西安市': 308,\n",
       " '铜川市': 309,\n",
       " '宝鸡市': 310,\n",
       " '咸阳市': 311,\n",
       " '渭南市': 312,\n",
       " '延安市': 313,\n",
       " '汉中市': 314,\n",
       " '榆林市': 315,\n",
       " '安康市': 316,\n",
       " '商洛市': 317,\n",
       " '兰州市': 318,\n",
       " '嘉峪关市': 319,\n",
       " '金昌市': 320,\n",
       " '白银市': 321,\n",
       " '天水市': 322,\n",
       " '武威市': 323,\n",
       " '张掖市': 324,\n",
       " '平凉市': 325,\n",
       " '酒泉市': 326,\n",
       " '庆阳市': 327,\n",
       " '定西市': 328,\n",
       " '陇南市': 329,\n",
       " '临夏回族自治州': 330,\n",
       " '甘南藏族自治州': 331,\n",
       " '西宁市': 332,\n",
       " '海东市': 333,\n",
       " '海北藏族自治州': 334,\n",
       " '黄南藏族自治州': 335,\n",
       " '海南藏族自治州': 336,\n",
       " '果洛藏族自治州': 337,\n",
       " '玉树藏族自治州': 338,\n",
       " '海西蒙古族藏族自治州': 339,\n",
       " '银川市': 340,\n",
       " '石嘴山市': 341,\n",
       " '吴忠市': 342,\n",
       " '固原市': 343,\n",
       " '中卫市': 344,\n",
       " '乌鲁木齐市': 345,\n",
       " '克拉玛依市': 346,\n",
       " '吐鲁番市': 347,\n",
       " '哈密市': 348,\n",
       " '昌吉回族自治州': 349,\n",
       " '博尔塔拉蒙古族自治州': 350,\n",
       " '巴音郭楞蒙古自治州': 351,\n",
       " '阿克苏地区': 352,\n",
       " '克孜勒苏柯尔克孜自治州': 353,\n",
       " '喀什地区': 354,\n",
       " '和田地区': 355,\n",
       " '伊犁哈萨克自治州': 356,\n",
       " '塔城地区': 357,\n",
       " '阿勒泰地区': 358,\n",
       " '石河子市': 359,\n",
       " '阿拉尔市': 360,\n",
       " '图木舒克市': 361,\n",
       " '五家渠市': 362,\n",
       " '北屯市': 363,\n",
       " '铁门关市': 364,\n",
       " '双河市': 365,\n",
       " '可克达拉市': 366,\n",
       " '昆玉市': 367,\n",
       " '台湾省': 368,\n",
       " '香港特别行政区': 369,\n",
       " '澳门特别行政区': 370}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读取2020年的百度部分流量比例数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cities</th>\n",
       "      <th>北京市</th>\n",
       "      <th>天津市</th>\n",
       "      <th>石家庄市</th>\n",
       "      <th>唐山市</th>\n",
       "      <th>秦皇岛市</th>\n",
       "      <th>邯郸市</th>\n",
       "      <th>邢台市</th>\n",
       "      <th>保定市</th>\n",
       "      <th>张家口市</th>\n",
       "      <th>...</th>\n",
       "      <th>和田地区</th>\n",
       "      <th>伊犁哈萨克自治州</th>\n",
       "      <th>塔城地区</th>\n",
       "      <th>阿勒泰地区</th>\n",
       "      <th>石河子市</th>\n",
       "      <th>阿拉尔市</th>\n",
       "      <th>五家渠市</th>\n",
       "      <th>香港特别行政区</th>\n",
       "      <th>澳门特别行政区</th>\n",
       "      <th>Unnamed: 352</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>inf</td>\n",
       "      <td>11.855277</td>\n",
       "      <td>5.725960</td>\n",
       "      <td>12.749903</td>\n",
       "      <td>9.573570</td>\n",
       "      <td>6.598759</td>\n",
       "      <td>4.861948</td>\n",
       "      <td>27.363624</td>\n",
       "      <td>37.854040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729644</td>\n",
       "      <td>0.424284</td>\n",
       "      <td>0.146950</td>\n",
       "      <td>0.244898</td>\n",
       "      <td>0.284529</td>\n",
       "      <td>0.192485</td>\n",
       "      <td>0.109344</td>\n",
       "      <td>1.716803</td>\n",
       "      <td>1.431816</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>4.458202</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.806775</td>\n",
       "      <td>20.849449</td>\n",
       "      <td>5.471267</td>\n",
       "      <td>5.470594</td>\n",
       "      <td>1.854504</td>\n",
       "      <td>3.523714</td>\n",
       "      <td>3.469709</td>\n",
       "      <td>...</td>\n",
       "      <td>0.527886</td>\n",
       "      <td>0.085204</td>\n",
       "      <td>0.012593</td>\n",
       "      <td>0.069344</td>\n",
       "      <td>0.005139</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>0.026867</td>\n",
       "      <td>0.228988</td>\n",
       "      <td>0.325382</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>2.230990</td>\n",
       "      <td>1.815094</td>\n",
       "      <td>inf</td>\n",
       "      <td>3.832100</td>\n",
       "      <td>3.980366</td>\n",
       "      <td>11.405949</td>\n",
       "      <td>30.187961</td>\n",
       "      <td>15.307815</td>\n",
       "      <td>5.608304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081481</td>\n",
       "      <td>0.003760</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.075537</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.044484</td>\n",
       "      <td>0.012442</td>\n",
       "      <td>0.007495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>1.755526</td>\n",
       "      <td>6.625320</td>\n",
       "      <td>2.427825</td>\n",
       "      <td>inf</td>\n",
       "      <td>23.561492</td>\n",
       "      <td>1.150411</td>\n",
       "      <td>0.838385</td>\n",
       "      <td>1.560130</td>\n",
       "      <td>2.586040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074082</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>0.743911</td>\n",
       "      <td>1.104723</td>\n",
       "      <td>0.999676</td>\n",
       "      <td>12.371138</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.347803</td>\n",
       "      <td>0.232399</td>\n",
       "      <td>0.585399</td>\n",
       "      <td>0.724584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>邯郸市</td>\n",
       "      <td>3.058310</td>\n",
       "      <td>3.685172</td>\n",
       "      <td>9.266465</td>\n",
       "      <td>2.033850</td>\n",
       "      <td>1.596207</td>\n",
       "      <td>inf</td>\n",
       "      <td>18.648848</td>\n",
       "      <td>2.832027</td>\n",
       "      <td>1.373215</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010470</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040079</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>邢台市</td>\n",
       "      <td>1.784559</td>\n",
       "      <td>1.592507</td>\n",
       "      <td>17.716423</td>\n",
       "      <td>1.595459</td>\n",
       "      <td>1.481331</td>\n",
       "      <td>17.228512</td>\n",
       "      <td>inf</td>\n",
       "      <td>2.246571</td>\n",
       "      <td>1.065858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009354</td>\n",
       "      <td>0.031075</td>\n",
       "      <td>0.004738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034814</td>\n",
       "      <td>0.009440</td>\n",
       "      <td>0.009669</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>保定市</td>\n",
       "      <td>7.296308</td>\n",
       "      <td>3.120498</td>\n",
       "      <td>14.208043</td>\n",
       "      <td>3.131967</td>\n",
       "      <td>2.768861</td>\n",
       "      <td>2.910327</td>\n",
       "      <td>2.589855</td>\n",
       "      <td>inf</td>\n",
       "      <td>5.916331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007716</td>\n",
       "      <td>0.005211</td>\n",
       "      <td>0.017470</td>\n",
       "      <td>0.010746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027492</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>张家口市</td>\n",
       "      <td>3.680067</td>\n",
       "      <td>1.204148</td>\n",
       "      <td>2.334679</td>\n",
       "      <td>1.417932</td>\n",
       "      <td>1.110539</td>\n",
       "      <td>0.612830</td>\n",
       "      <td>0.439005</td>\n",
       "      <td>2.174117</td>\n",
       "      <td>inf</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>承德市</td>\n",
       "      <td>2.454110</td>\n",
       "      <td>0.976658</td>\n",
       "      <td>1.627907</td>\n",
       "      <td>6.065174</td>\n",
       "      <td>3.048455</td>\n",
       "      <td>0.362100</td>\n",
       "      <td>0.222737</td>\n",
       "      <td>1.148757</td>\n",
       "      <td>2.144588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004509</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 353 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cities       北京市        天津市       石家庄市        唐山市       秦皇岛市        邯郸市  \\\n",
       "0    北京市       inf  11.855277   5.725960  12.749903   9.573570   6.598759   \n",
       "1    天津市  4.458202        inf   1.806775  20.849449   5.471267   5.470594   \n",
       "2   石家庄市  2.230990   1.815094        inf   3.832100   3.980366  11.405949   \n",
       "3    唐山市  1.755526   6.625320   2.427825        inf  23.561492   1.150411   \n",
       "4   秦皇岛市  0.743911   1.104723   0.999676  12.371138        inf   0.347803   \n",
       "5    邯郸市  3.058310   3.685172   9.266465   2.033850   1.596207        inf   \n",
       "6    邢台市  1.784559   1.592507  17.716423   1.595459   1.481331  17.228512   \n",
       "7    保定市  7.296308   3.120498  14.208043   3.131967   2.768861   2.910327   \n",
       "8   张家口市  3.680067   1.204148   2.334679   1.417932   1.110539   0.612830   \n",
       "9    承德市  2.454110   0.976658   1.627907   6.065174   3.048455   0.362100   \n",
       "\n",
       "         邢台市        保定市       张家口市  ...      和田地区  伊犁哈萨克自治州      塔城地区  \\\n",
       "0   4.861948  27.363624  37.854040  ...  0.729644  0.424284  0.146950   \n",
       "1   1.854504   3.523714   3.469709  ...  0.527886  0.085204  0.012593   \n",
       "2  30.187961  15.307815   5.608304  ...  0.000000  0.081481  0.003760   \n",
       "3   0.838385   1.560130   2.586040  ...  0.000000  0.000000  0.000000   \n",
       "4   0.232399   0.585399   0.724584  ...  0.000000  0.000000  0.000000   \n",
       "5  18.648848   2.832027   1.373215  ...  0.000000  0.010470  0.000000   \n",
       "6        inf   2.246571   1.065858  ...  0.009354  0.031075  0.004738   \n",
       "7   2.589855        inf   5.916331  ...  0.000000  0.007716  0.005211   \n",
       "8   0.439005   2.174117        inf  ...  0.000000  0.000000  0.000000   \n",
       "9   0.222737   1.148757   2.144588  ...  0.000000  0.000000  0.000000   \n",
       "\n",
       "      阿勒泰地区      石河子市      阿拉尔市      五家渠市   香港特别行政区   澳门特别行政区  Unnamed: 352  \n",
       "0  0.244898  0.284529  0.192485  0.109344  1.716803  1.431816           NaN  \n",
       "1  0.069344  0.005139  0.027000  0.026867  0.228988  0.325382           NaN  \n",
       "2  0.000000  0.075537  0.006981  0.044484  0.012442  0.007495           NaN  \n",
       "3  0.000000  0.012455  0.000000  0.000000  0.000000  0.074082           NaN  \n",
       "4  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000           NaN  \n",
       "5  0.000000  0.040079  0.000000  0.000000  0.000000  0.000000           NaN  \n",
       "6  0.000000  0.034814  0.009440  0.009669  0.000000  0.000000           NaN  \n",
       "7  0.017470  0.010746  0.000000  0.027492  0.000000  0.000000           NaN  \n",
       "8  0.000000  0.000000  0.000000  0.005701  0.000000  0.000000           NaN  \n",
       "9  0.000000  0.000000  0.004509  0.000000  0.000000  0.000000           NaN  \n",
       "\n",
       "[10 rows x 353 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Pij_BAIDU.csv',encoding='gbk')\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 4.45820151e-02, 2.23099039e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.18552769e-01, 0.00000000e+00, 1.81509430e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [5.72596015e-02, 1.80677508e-02, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.71680253e-02, 2.28988485e-03, 1.24418856e-04, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 2.05991973e-02],\n",
       "       [1.43181560e-02, 3.25381944e-03, 7.49467787e-05, ...,\n",
       "        0.00000000e+00, 1.74395725e-02, 0.00000000e+00]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = {d:i for i,d in enumerate(df['Cities'])}\n",
    "pij2 = np.zeros([len(nodes), len(nodes)])\n",
    "for k,ind in cities.items():\n",
    "    row = df[k]\n",
    "    for city,column in cities.items():\n",
    "        i_indx = nodes.get(city, '-1')\n",
    "        if i_indx < 0:\n",
    "            print(city)\n",
    "        j_indx = nodes.get(k, '-1')\n",
    "        if j_indx < 0:\n",
    "            print(k)\n",
    "        if i_indx >=0 and j_indx >= 0:\n",
    "            pij2[j_indx, i_indx] = row[column] / 100\n",
    "            if i_indx == j_indx:\n",
    "                pij2[i_indx, j_indx] = 0\n",
    "pij2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 将pij1和pij2合并\n",
    "合并思路，如果ij元素中pij2有值就用pij2的，否则将这一行所有的未被替换的pij1的值重新按照这些非零元素的数值大小计算占比，然后用1-$\\sum_{j}p_{ij2}$的总值乘以这个占比，从而得到新的数值，保证$\\sum_{j}p_{ij}=1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools = pij2 <= 0\n",
    "pij = np.zeros([pij1.shape[0], pij1.shape[0]])\n",
    "for i in range(pij1.shape[0]):\n",
    "    row = pij1[i]\n",
    "    bool1 = bools[i]\n",
    "    values = row * bool1\n",
    "    if np.sum(values) > 0:\n",
    "        ratios = values / np.sum(values)\n",
    "        sum2 = np.sum(pij2[i, :])\n",
    "        pij[i,:] = (1 - sum2) * ratios + pij2[i, :]\n",
    "np.sum(pij,1) #验证一下是否归一化\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('pij.pkl', 'wb')\n",
    "pickle.dump(pij, f, pickle.HIGHEST_PROTOCOL)\n",
    "f.close()\n",
    "f = open('nodes.pkl', 'wb')\n",
    "pickle.dump(nodes, f, pickle.HIGHEST_PROTOCOL)\n",
    "f.close()\n",
    "\n",
    "f = open('city_info.pkl', 'wb')\n",
    "pickle.dump(city_properties, f, pickle.HIGHEST_PROTOCOL)\n",
    "f.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 如果没有原始数据，可以从这里开始\n",
    "加载数据:pij, nodes, city_properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('pij.pkl', 'rb')\n",
    "pij = pickle.load(f)\n",
    "f.close()\n",
    "f = open('nodes.pkl', 'rb')\n",
    "nodes = pickle.load(f)\n",
    "f.close()\n",
    "f = open('city_info.pkl', 'rb')\n",
    "city_properties = pickle.load(f)\n",
    "f.close()\n",
    "flowing_ratio = 0.1255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取病例数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>确诊/出院</th>\n",
       "      <th>公开报道时间</th>\n",
       "      <th>省份</th>\n",
       "      <th>城市</th>\n",
       "      <th>新增确诊病例</th>\n",
       "      <th>新增治愈出院数</th>\n",
       "      <th>新增死亡数</th>\n",
       "      <th>消息来源</th>\n",
       "      <th>来源链接1</th>\n",
       "      <th>来源链接2</th>\n",
       "      <th>来源链接3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>315</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>黄石市</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>十堰市</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>襄阳市</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>宜昌市</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>荆州市</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>荆门市</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>鄂州市</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>孝感市</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>黄冈市</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>咸宁市</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>随州市</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>恩施州</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>仙桃市</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>天门市</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>潜江市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>神农架林区</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>湖北</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>湖北卫健委</td>\n",
       "      <td>http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>山西</td>\n",
       "      <td>运城市</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>山西卫计委</td>\n",
       "      <td>http://m.news.cctv.com/2020/01/29/ARTIVuFq7IxW...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>山西</td>\n",
       "      <td>晋中市</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>山西卫计委</td>\n",
       "      <td>http://m.news.cctv.com/2020/01/29/ARTIVuFq7IxW...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>山西</td>\n",
       "      <td>大同市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>山西卫计委</td>\n",
       "      <td>http://m.news.cctv.com/2020/01/29/ARTIVuFq7IxW...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>德国</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>央视</td>\n",
       "      <td>http://m.news.cctv.com/2020/01/29/ARTIKBTrjRkh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>南昌市</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>新余市</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>九江市</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>赣州市</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>宜春市</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>上饶市</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>吉安市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>江西</td>\n",
       "      <td>抚州市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>江西卫计委</td>\n",
       "      <td>https://wap.peopleapp.com/article/5083994/4979021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>重庆</td>\n",
       "      <td>长寿区</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>国家卫健委</td>\n",
       "      <td>https://mbd.baidu.com/newspage/data/landingsha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>四川</td>\n",
       "      <td>成都市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>国家卫健委</td>\n",
       "      <td>https://m.bjnews.com.cn/detail/157961692015750...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>北京</td>\n",
       "      <td>丰台区</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>北京市卫健委</td>\n",
       "      <td>http://wjw.beijing.gov.cn/xwzx_20031/wnxw/2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>北京</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>北京市卫健委</td>\n",
       "      <td>http://wjw.beijing.gov.cn/xwzx_20031/wnxw/2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>北京</td>\n",
       "      <td>西城区</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>北京市卫健委</td>\n",
       "      <td>http://wjw.beijing.gov.cn/xwzx_20031/wnxw/2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>北京</td>\n",
       "      <td>通州区</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>北京市卫健委</td>\n",
       "      <td>http://wjw.beijing.gov.cn/xwzx_20031/wnxw/2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>北京</td>\n",
       "      <td>海淀区</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>北京市卫健委</td>\n",
       "      <td>http://wjw.beijing.gov.cn/xwzx_20031/wnxw/2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>云南</td>\n",
       "      <td>昆明市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>昆明卫健委公号</td>\n",
       "      <td>https://mp.weixin.qq.com/s/9F-8GVfVxelda-aiK0Quzg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>山东</td>\n",
       "      <td>青岛市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>山东卫健委公号</td>\n",
       "      <td>https://mp.weixin.qq.com/s/iMF7880p_E9HW1uWgPjr3w</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>牡丹江市</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>牡丹江市卫生健康委</td>\n",
       "      <td>https://m.thepaper.cn/newsDetail_forward_5591330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>北京</td>\n",
       "      <td>大兴区</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>北京市大兴区卫健委</td>\n",
       "      <td>https://m.weibo.cn/2703012010/4462638756717942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>北京</td>\n",
       "      <td>昌平区</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>北京市卫健委</td>\n",
       "      <td>http://wjw.beijing.gov.cn/xwzx_20031/wnxw/2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>北京</td>\n",
       "      <td>外地来京</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://wjw.beijing.gov.cn/xwzx_20031/wnxw/2020...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>深圳市卫健委</td>\n",
       "      <td>http://wjw.sz.gov.cn/gsgg/202001/t20200120_189...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>广东卫健委</td>\n",
       "      <td>http://wsjkw.gd.gov.cn/zwyw_yqxx/content/post_...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>广东</td>\n",
       "      <td>珠海市</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>珠海市卫健委</td>\n",
       "      <td>http://wsjkj.zhuhai.gov.cn/zwgk/tzgg/content/p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>广东</td>\n",
       "      <td>湛江市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>广东卫健委</td>\n",
       "      <td>http://wsjkw.gd.gov.cn/zwyw_yqxx/content/post_...</td>\n",
       "      <td>https://www.zhanjiang.gov.cn/zjwjj/sy/gzdt/con...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>广东</td>\n",
       "      <td>惠州市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>广东卫健委</td>\n",
       "      <td>http://wsjkw.gd.gov.cn/zwyw_yqxx/content/post_...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>国家卫建委</td>\n",
       "      <td>https://m.bjnews.com.cn/detail/157952141515817...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>四川</td>\n",
       "      <td>成都市</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>成都市卫健委</td>\n",
       "      <td>http://cdwjw.chengdu.gov.cn/cdwjw/gzdt/2020-01...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>日本</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>武汉市卫健委、日本厚生劳动省</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>https://news.un.org/zh/story/2020/01/1049342</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉市</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>武汉市卫健委</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>http://wjw.wuhan.gov.cn/front/web/showDetail/2...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>泰国</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>泰国公共卫生部</td>\n",
       "      <td>http://china.caixin.com/2020-01-17/101506051.html</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          确诊/出院     公开报道时间  省份   城市  新增确诊病例  新增治愈出院数  新增死亡数     消息来源  \\\n",
       "0    2020-01-28 2020-01-29  湖北  武汉市     315        0     19    湖北卫健委   \n",
       "1    2020-01-28 2020-01-29  湖北  黄石市      33        0      0    湖北卫健委   \n",
       "2    2020-01-28 2020-01-29  湖北  十堰市      23        0      0    湖北卫健委   \n",
       "3    2020-01-28 2020-01-29  湖北  襄阳市      61        0      0    湖北卫健委   \n",
       "4    2020-01-28 2020-01-29  湖北  宜昌市      12        0      0    湖北卫健委   \n",
       "...         ...        ...  ..  ...     ...      ...    ...      ...   \n",
       "1095 2020-01-15 2020-01-16  湖北  武汉市       0        5      1   武汉市卫健委   \n",
       "1096 2020-01-12 2020-01-13  湖北  武汉市       0        1      0   武汉市卫健委   \n",
       "1097 2020-01-11 2020-01-12  湖北  武汉市       0        4      0   武汉市卫健委   \n",
       "1098 2020-01-10 2020-01-11  湖北  武汉市      41        2      1   武汉市卫健委   \n",
       "1099 2020-01-17        NaT  泰国  NaN       2        0      0  泰国公共卫生部   \n",
       "\n",
       "                                                  来源链接1  \\\n",
       "0     http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...   \n",
       "1     http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...   \n",
       "2     http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...   \n",
       "3     http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...   \n",
       "4     http://wjw.hubei.gov.cn/fbjd/dtyw/202001/t2020...   \n",
       "...                                                 ...   \n",
       "1095  http://wjw.wuhan.gov.cn/front/web/showDetail/2...   \n",
       "1096  http://wjw.wuhan.gov.cn/front/web/showDetail/2...   \n",
       "1097  http://wjw.wuhan.gov.cn/front/web/showDetail/2...   \n",
       "1098  http://wjw.wuhan.gov.cn/front/web/showDetail/2...   \n",
       "1099  http://china.caixin.com/2020-01-17/101506051.html   \n",
       "\n",
       "                                                  来源链接2 来源链接3  \n",
       "0                                                   NaN   NaN  \n",
       "1                                                   NaN   NaN  \n",
       "2                                                   NaN   NaN  \n",
       "3                                                   NaN   NaN  \n",
       "4                                                   NaN   NaN  \n",
       "...                                                 ...   ...  \n",
       "1095                                                NaN   NaN  \n",
       "1096                                                NaN   NaN  \n",
       "1097                                                NaN   NaN  \n",
       "1098  http://wjw.wuhan.gov.cn/front/web/showDetail/2...   NaN  \n",
       "1099                                                NaN   NaN  \n",
       "\n",
       "[1100 rows x 11 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader=pd.ExcelFile('city_cases.xlsx')\n",
    "df = reader.parse(\"Sheet1\")\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-10 00:00:00 39\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_cases_cities = list(set(df['城市']))[1:]\n",
    "all_cases_cities = all_cases_cities + ['北京市','天津市','重庆市','上海市']\n",
    "#print(all_cases_cities)\n",
    "wuhan=df.loc[df['城市']=='武汉市',['新增确诊病例','确诊/出院','公开报道时间','新增治愈出院数','新增死亡数']]\n",
    "dates = list(wuhan['确诊/出院'])\n",
    "sorted_dates = np.sort(dates)\n",
    "first_date = sorted_dates[0]\n",
    "first_cases = 39\n",
    "print(first_date, first_cases)\n",
    "\n",
    "all_cases = {}\n",
    "\n",
    "for city in all_cases_cities:\n",
    "    subset = df.loc[df['城市']==city,['新增确诊病例','确诊/出院','公开报道时间','新增治愈出院数','新增死亡数','省份']]\n",
    "    zhixia = False\n",
    "    if len(subset)==0:\n",
    "        subset = df.loc[df['省份']==city[:-1],['新增确诊病例','确诊/出院','公开报道时间','新增治愈出院数','新增死亡数','省份']]\n",
    "        zhixia = True\n",
    "    new_cases = np.array(subset['新增确诊病例'])\n",
    "    cued_cases = np.array(subset['新增治愈出院数'])\n",
    "    die_cases = np.array(subset['新增死亡数'])\n",
    "    dates = list(subset['确诊/出院'])\n",
    "    dates1 = list(subset['公开报道时间'])\n",
    "    days = []\n",
    "    for i,dd in enumerate(dates):\n",
    "        if pd.isna(dd):\n",
    "            dd = dates1[i]\n",
    "        if not pd.isna(dd):\n",
    "            days.append(int((dd - first_date)/ np.timedelta64(1, 'D')))\n",
    "    sorted_days = np.sort(days)\n",
    "    indx = np.argsort(days)\n",
    "    infected = np.cumsum(new_cases[indx])\n",
    "    cued = np.cumsum(cued_cases[indx])\n",
    "    death = np.cumsum(die_cases[indx])\n",
    "    if len(sorted_days)>0:\n",
    "        if zhixia:\n",
    "            all_cases[list(subset['省份'])[0]+'市']= (sorted_days, infected, cued, death)\n",
    "        else:\n",
    "            all_cases[city] = (sorted_days, infected, cued, death)\n",
    "for case in all_cases.values():\n",
    "    xx = case[0]\n",
    "    yy = case[1]\n",
    "    if len(yy) > 0:\n",
    "        plt.semilogy(xx,yy,'o-')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>新增确诊病例</th>\n",
       "      <th>确诊/出院</th>\n",
       "      <th>公开报道时间</th>\n",
       "      <th>新增治愈出院数</th>\n",
       "      <th>新增死亡数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>315</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>892</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>80</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>46</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>77</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>70</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>105</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>62</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>60</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>59</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>77</td>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>41</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      新增确诊病例      确诊/出院     公开报道时间  新增治愈出院数  新增死亡数\n",
       "0        315 2020-01-28 2020-01-29        0     19\n",
       "83       892 2020-01-27 2020-01-28        0     22\n",
       "199        0 2020-01-28 2020-01-28        3      0\n",
       "262       80 2020-01-26 2020-01-27        2     18\n",
       "476       46 2020-01-25 2020-01-26        8      7\n",
       "649       77 2020-01-24 2020-01-25        1     15\n",
       "837       70 2020-01-23 2020-01-24        0      6\n",
       "936        0 2020-01-23 2020-01-24        3      0\n",
       "1019       0 2020-01-21 2020-01-22        3      3\n",
       "1022     105 2020-01-21 2020-01-22        0      0\n",
       "1041      62 2020-01-22 2020-01-22        0      8\n",
       "1055      60 2020-01-20 2020-01-21        0      2\n",
       "1088      59 2020-01-18 2020-01-20        5      1\n",
       "1089      77 2020-01-19 2020-01-20        1      1\n",
       "1092      17 2020-01-17 2020-01-19        4      0\n",
       "1093       4 2020-01-16 2020-01-18        3      0\n",
       "1095       0 2020-01-15 2020-01-16        5      1\n",
       "1096       0 2020-01-12 2020-01-13        1      0\n",
       "1097       0 2020-01-11 2020-01-12        4      0\n",
       "1098      41 2020-01-10 2020-01-11        2      1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wuhan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3601036269430052"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cued = 0\n",
    "death = 0\n",
    "for k,itms in all_cases.items():\n",
    "    cued += np.sum(itms[2])\n",
    "    death += np.sum(itms[3])\n",
    "death_ratio = death / (death+cued)\n",
    "death_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #读取工作簿和工作簿中的工作表\n",
    "# reader=pd.ExcelFile('Wuhan_nCoV202001241408.xlsx')\n",
    "# df = reader.parse(\"汇总表格\")\n",
    "# all_cases_cities = list(set(df['地级行政单位']))[1:]\n",
    "# wuhan=df.loc[df['地级行政单位']=='武汉',['病例数','发病日期','确诊日期','地级行政单位']]\n",
    "# dates = list(wuhan['发病日期'])\n",
    "# first_date = dates[0]\n",
    "# print(first_date)\n",
    "\n",
    "# all_cases = {}\n",
    "# for city in all_cases_cities:\n",
    "#     subset = df.loc[df['地级行政单位']==city,['病例数','发病日期','确诊日期']]\n",
    "#     a = np.array(subset['病例数'])\n",
    "#     dates = list(subset['发病日期'])\n",
    "#     dates1 = list(subset['确诊日期'])\n",
    "\n",
    "#     days = []\n",
    "#     for i,dd in enumerate(dates):\n",
    "#         #if pd.isna(dd):\n",
    "#         #    dd = dates1[i]\n",
    "#         if not pd.isna(dd):\n",
    "#             days.append(int((dd - first_date)/ np.timedelta64(1, 'D')))\n",
    "#     sorted_days = np.sort(days)\n",
    "#     indx = np.argsort(days)\n",
    "#     infected = np.cumsum(a[indx])\n",
    "#     if len(sorted_days)>0:\n",
    "#         all_cases[city] = (sorted_days, infected)\n",
    "# for case in all_cases.values():\n",
    "#     xx = case[0]\n",
    "#     yy = case[1]\n",
    "#     if len(yy) > 0:\n",
    "#         plt.semilogy(xx,yy,'o-')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 有效距离"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了计算有效距离，需要重新修改矩阵乘积的法则，基本思路就是在计算概率的时候，要把加法变为求大，计算距离的时候，要求小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_matrix_production(matrix, matrix1):\n",
    "    #特殊的矩阵乘积，将加法转换为取大\n",
    "    sz = matrix.shape[0]\n",
    "    output = np.zeros([sz, sz])\n",
    "    for i in range(sz):\n",
    "        for j in range(sz):\n",
    "            row = matrix[i, :]\n",
    "            col = matrix1[:, j].reshape(-1)\n",
    "            prod = row * col\n",
    "            ele = np.amax(prod)\n",
    "            output[i,j]=ele\n",
    "    return output\n",
    "def eff_distance(prob):\n",
    "    #输入迁移概率矩阵，计算有效距离\n",
    "    sz = prob.shape[0]\n",
    "    prod = prob\n",
    "    distance = np.ones([sz, sz]) - np.log(prod)\n",
    "    for i in range(1, sz - 1):\n",
    "        flushPrint(i / sz)\n",
    "        prod = my_matrix_production(prod, prob)\n",
    "        dist = i + 1 - np.log(prod)\n",
    "        distance = np.minimum(distance, dist)\n",
    "    return distance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 利用概率转移矩阵，计算有效距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "0.0026954177897574125"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhangjiang/anaconda3/envs/deeplearning/lib/python3.6/site-packages/ipykernel_launcher.py:17: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "0.005390835579514825"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhangjiang/anaconda3/envs/deeplearning/lib/python3.6/site-packages/ipykernel_launcher.py:21: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.994609164420485276"
     ]
    }
   ],
   "source": [
    "effective_distance = eff_distance(pij)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#outfile = open('effective_distance.npy', 'w')\n",
    "outfile = TemporaryFile()\n",
    "np.save(outfile, effective_distance)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 计算出武汉到每个城市的有效距离"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "距离排序并显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "eff_dist = copy.deepcopy(effective_distance)\n",
    "for i in range(len(nodes)):\n",
    "    eff_dist[i,i]=np.inf\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rowid = nodes['武汉市']\n",
    "sorted_distance = np.sort(eff_dist[rowid,:])\n",
    "sorted_cities = np.argsort(eff_dist[rowid,:])\n",
    "sorted_flux = np.sort(pij[rowid,:])[::-1]\n",
    "sorted_flux_cities = np.argsort(pij[rowid,:])[::-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "武汉市 inf\n",
      "三沙市 inf\n",
      "东沙群岛 inf\n"
     ]
    }
   ],
   "source": [
    "def find_in_nodes(ind, nodes):\n",
    "    key = -1\n",
    "    for k,v in nodes.items():\n",
    "        if v == ind:\n",
    "            key = k\n",
    "            break\n",
    "    return key\n",
    "firsts=[]\n",
    "firsts_dis=[]\n",
    "city_names_dis=[]\n",
    "for n, i in enumerate(sorted_cities):\n",
    "    city_name = find_in_nodes(i, nodes)\n",
    "    itm = all_cases.get(city_name,[])\n",
    "    if len(itm)==0:\n",
    "        itm = all_cases.get(city_name[:-1],[])\n",
    "    if len(itm)>0:\n",
    "        firsts.append(itm[0][0])\n",
    "        firsts_dis.append(sorted_distance[n])\n",
    "        city_names_dis.append(city_name)\n",
    "    print(city_name, sorted_distance[n])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "昌江黎族自治县 4.6502448970675006e-05\n",
      "和田地区 4.6502448970675006e-05\n",
      "双河市 3.720195917654e-05\n",
      "怒江傈僳族自治州 3.720195917654e-05\n",
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      "北屯市 2.7901469382405003e-05\n",
      "五家渠市 1.860097958827e-05\n",
      "果洛藏族自治州 1.860097958827e-05\n",
      "黄南藏族自治州 1.860097958827e-05\n",
      "白沙黎族自治县 1.860097958827e-05\n",
      "克孜勒苏柯尔克孜自治州 1.860097958827e-05\n",
      "保亭黎族苗族自治县 1.860097958827e-05\n",
      "玉树藏族自治州 9.300489794135e-06\n",
      "图木舒克市 9.300489794135e-06\n",
      "阿里地区 9.300489794135e-06\n",
      "铁门关市 9.300489794135e-06\n",
      "琼中黎族苗族自治县 9.300489794135e-06\n",
      "大兴安岭地区 9.300489794135e-06\n",
      "五指山市 9.300489794135e-06\n",
      "武汉市 0.0\n",
      "昆玉市 0.0\n",
      "三沙市 0.0\n",
      "东沙群岛 0.0\n"
     ]
    }
   ],
   "source": [
    "for n, i in enumerate(sorted_flux_cities):\n",
    "    city_name = find_in_nodes(i, nodes)\n",
    "    print(city_name, sorted_flux[n])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'pij')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEGCAYAAABy53LJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAeXklEQVR4nO3dfZRcdZ3n8feHTgJNBINLYJYCJlFifAqknVaIGcfoGJOFIC3jGh3d465sWD0HZyMz2UkWdhg4MImTHWQc3EV0EN1ECLLZnmjUkBmWwRMJpiGBwECGBzF049BxQ3hsSGi++0fdgkqnuh46XXWrbn1e59Tprlu3bn1LEz65v0dFBGZmZtU6Iu0CzMystTg4zMysJg4OMzOriYPDzMxq4uAwM7OaTEi7gHo4/vjjY9q0aWmXYWbWUu65557fRMTUSudlMjimTZtGX19f2mWYmbUUSb+q5jw3VZmZWU0yFRySzpV0/bPPPpt2KWZmmZWp4IiIH0bEhW9+85vTLsXMLLMyFRxmZlZ/Dg4zM6tJJkdVjUXv9gFWb9rFU/uGOGlKJ8sWzKSnK5d2WWZmTcd3HORDY8X6nQzsGyKAgX1DLF23g64rbqN3+0Da5ZmZNRUHB7B60y6GDgwfcvyZlw6wYv1Oh4eZWREHB/DUvqFRXxs6MMzqTbsaWI2ZWXNzcAAnTeks+3q5YDEzazcODmDZgpl0TuwY9fVKwWJm1k4cHEBPV46V589iSufEQ17rnNjBsgUzU6jKzKw5NX1wSHqrpL+VdGs9P6enK8eOyz7GNYtnk5vSiYDclE5Wnj/Lw3LNzIrUdR6HpBuARcBgRLyn6PhC4K+BDuDbEbFqtGtExOPABfUOjoKerpyDwsysjHpPALwRuBb4XuGApA7gG8B8oB/YJmkD+RBZOeL9X4iIwTrXaGZmNahrcETEnZKmjTj8fuDR5E4CSTcD50XESvJ3J2Mi6ULgQoBTTz11rJcxM7MK0ujjyAFPFj3vT46VJOlfSboO6JK0YrTzIuL6iOiOiO6pUytuYGVmZmOUxlpVKnEsRjs5Iv4f8MWqLiydC5x72mmnjbE0MzOrJI07jn7glKLnJwNPjceFvR+HmVn9pREc24AZkqZLmgR8GtgwHhf2DoBmZvVX1+CQdBNwFzBTUr+kCyLiVeAiYBPwEHBLRDw4Hp/nOw4zs/qr96iqz4xy/MfAj+v52Wnxvh5mlnWZ2sgp7c7xwr4ehSXaB/YNsWL9TgCHh5llRtMvOVKLtJuqSu3r4WXZzSxrMhUcaRtt+XUvy25mWZKp4Eh7VNVoy697WXYzy5JMBUfaTVWl9vUoXpa9d/sAc1fdzvTlG5m76nZvSWtmLSlTneNpK3SAlxpV5Y5zM8sKB8c4G21Z9nId5w4OM2slmWqqSruPoxx3nJtZVmQqONLu4yjHHedmlhVuqmqQZQtmHtTHAfmO8w+/YypdV9zGMy8dAGBK50T+/OPvdvOVmTWtTN1xNLOerhwrz5910H7mf/A7OdZte/L10ADYN3SApet2cGnvzvSKNTMrw3ccDTSy43zuqts5MFx6K5K1W3fT/dtv8Z2HmTWdTN1xNHPneCnlOsYDvFSJmTWlTAVHM3eOl1KpY3xg3xDTlm+k64rbPFnQzJpGpoKj1SxbMJOJHaV20j3YMy8dYNmt9zk8zKwpODhS1NOVY/Unz2DypI6K5x4YDv58w7jsd2VmdlgcHCnr6crx4BULuWbxbHIVmq72DR1g9uVutjKzdDk4mkRPV44tyz9SVXisWL/T4WFmqclUcLTaqKpSli2YycQjyvd7eHMoM0tTpoKj1UZVldLTlWP1vz0DVegz9xpXZpaWTAVHVvR05fjap2YfsrdHMa9xZWZp8czxJlWYMX75Dx88aEkSOHhzKDOzRnNwNLHCEiW92wdKbg4FlH3NzKweHBwtYLTNobyroJmlwX0cLWy0XQWXrtvhPc3NrG4cHC2s3MiqgX1DLPuBlykxs/GXqeDIwjyOWlQaWXXgNS9TYmbjL1PBkYV5HLVYtmBm2SG7kJ9pbmY2ntw53sIKHeCrN+1iwBMCzaxBFFF6B7pW1t3dHX19fWmX0VDF+5YXmzypgylHT/JwXTOrSNI9EdFd6bxMNVW1s8vOffche3t0HCH2v/oaA/uGCN4YrusOczM7HA6OjCjs7ZGb0omA3JROjjlyAgdeO/iO0gskmtnhclNVhk1fvpHR/t8VuOnKzA7ipiorO1zXTVdmNlYOjgyrZrium67MrFYOjgzr6cqx8vxZr/d7jGZg35CXKDGzqnkeR8YVL5A4d9Xto8738AKJZlatlrjjkNQj6VuS/k7Sx9Kup1VVarryAolmVo26B4ekGyQNSnpgxPGFknZJelTS8nLXiIjeiFgC/HtgcR3LzbTipqty3GluZuU04o7jRmBh8QFJHcA3gH8DvAv4jKR3SZol6UcjHicUvfXS5H02Rj1dObYs/0jF8HCnuZmNpu7BERF3AntHHH4/8GhEPB4R+4GbgfMiYmdELBrxGFTeV4GfRMS9pT5H0oWS+iT17dmzp75fKgOqGXHlTnMzKyWtPo4c8GTR8/7k2Gi+DHwU+KSkL5Y6ISKuj4juiOieOnXq+FWaUW62MrOxSis4So0OHXUKe0R8PSJ+JyK+GBHXjXrRNtuP43AVmq2uWTy7Yqe5m63MrCCt4OgHTil6fjLw1OFetN324xgv1dx9lNtt0MzaS1rBsQ2YIWm6pEnAp4ENKdViVO40r7TboJm1j0YMx70JuAuYKalf0gUR8SpwEbAJeAi4JSIOe49TN1UdvlKd5p0TO1i2YCa92weYu+p2pi/f6E5zszbm1XHtEL3bB1i9addBmz8BrFi/k6EDwwedO/dtb2HtkjlplGlm46za1XEdHFaVcsuVfO6sU7myZ1aDKzKz8daWy6q7qap+ynWOr9m6201XZm0kU8HhUVX1U6lz3PM9zNpHpoLD6qfQz1GO53uYtYdMBYebquqnpyvH3Le9peJ5A/uGeNuKH3Np784GVGVmachUcLipqr7WLpnD5846lQ6V2xYKhiNYs3U386++ozGFmVlDeVSVjUnv9oGSw3NL8ZBds9bQlqOqrHGqXSQRYMtje333YZYhmQoO93E0VmGZkkpNVwCPDL7oEVdmGZGp4HAfRzo+c+YplU8C/viW+xweZhmQqeCwdFzZM4sZJ0yueN5whOd6mGWAg8PGxeaL51U1XNdzPcxaX6aCw30c6Vq7ZA7XLJ5d8Q/VwL4hr7Br1sI8HNfqonf7AH98y30MV/Hna8YJk9l88bz6F2VmZXk4rqWqpyvHX33qjLJb0hY8Mvgin/3WXQ2oyszGw4S0C7Ds6unKAby+t0e5e48tj+1tTFFmdth8x2F1VZjr8ctV51Q8130eZq3BwWENc9zRE8u+PrBviKXrdtB1xW0OELMmlqng8Kiq5nbZue+m8hxzeOalAyz7gScLmjWrTAWHZ443t56uHF9bPJsjJ1T+Y3fgteAr63Y4PMyakIfjWmrK7WN+0HleXdesITwc15resgUzqxqu69V1zZqLg8NSU1iavYrFdT3Xw6yJODgsVT1dOb72qdlM7KicHlse28s7/9tP3O9hljIHh6WupyvH6k+eUXG4LsDQgddYum6H9zQ3S5GDw5pCT1eO7X/2sapW2AVYs3W3w8MsJZkKDs/jaH1rl8ypam8PyIeHm67MGi9TweF5HNlQ7d4ekG+6utjzPcwaKlPBYdmxdskcPnfWqVWd+xp4sqBZAzk4rGld2TOLaxbPZvKkynM9Ali6boeH7Jo1gIPDmlpPV44Hr1hY9d3Hlsf2utPcrM4cHNYSruyZVXV4rNm6m2nLNzpAzOrEwWEto5bwAA/ZNasXB4e1lEK/R7XWbN3tTnOzcebgsJbT05Wrergu4JnmZuOsbHBIOjb5+ZYSj+MkVR7uYlYHa5fMqSk81mzdzZlXba5jRWbto9Idx/eTn/cAfcnPwuNe4F8k/UX9yjMb3dolc3hi1TlVB8jTz+/ntBUb61yVWfaVDY6IWJT8nB4Rb01+vv4Afgv4RD0LlPROSddJulXSl+r5Wdaaarn7eDVg2vKN7vcwOwyVmqrekfx8b6lHRAxHxDvLvP8GSYOSHhhxfKGkXZIelbS8XA0R8VBEfBH4FFBxZyprT2uXzKmp03ypZ5qbjdmECq9fDFwI/BX5ybkFSp5/pML7bwSuBb73+hvz/SLfAOYD/cA2SRuADmDliPd/ISIGJX0cWJ5cy6yknq4cP+jbzZbH9lZ1/tJ1O+j71V6u7JlV58rMsqVSU9WFya9nAxuBZ4F9wIbkWFkRcScw8m/x+4FHI+LxiNgP3AycFxE7I2LRiMdgcp0NEfEB4LO1fDlrP7WscQXuNDcbi2qH434XeCfwdeBvkt+/V/Ydo8sBTxY970+OlSRpnqSvS/om8OMy510oqU9S3549e8ZYmmVBrXM9nn5+v8PDrAaVmqoKZkbEGUXP/6+k+8b4maX2CI0Sx/IvRNwB3FHpohFxPXA9QHd396jXs/bQ05X/t8jSdTuqOv/p5/dz+mU/5f7LF9azLLNMqPaOY7ukswpPJJ0JbBnjZ/YDpxQ9Pxl4aozXOog3crJiPV05nlh1DkdVsZ85wHOvDDNt+UavsGtWQbXBcSbwc0lPSHoCuAv4kKSdku6v8TO3ATMkTZc0Cfg0+T6Tw+aNnKyUh686mxOPmVT1+Vse28vpl/20jhWZtbZqg2MhMB34UPKYTr5zfBFw7mhvknQT+ZCZKalf0gUR8SpwEbAJeAi4JSIeHPtXOOjzfMdhJd19yfyaZpo/98qwJwuajUIR2esO6O7ujr6+vrTLsCZ0ae9O1mzdXdN7nlh1Tp2qMWsuku6JiIrz5bzIobWVWkdcQX6muZm9IVPB4aYqq0ZPV25M4eFOc7O8TAWHO8etWmMJjy2P7fV8DzMyFhxmtah1uC7k53u845JR56GatYVMBYebqmwsHr7q7JqWKXl5ODxc19qaR1WZFamlI3zGCZPZfPG8+hVj1mAeVWU2BrU0XT0y+CLTlm/0trTWdhwcZiM8fNXZ3pbWrIxMBYf7OGy8rF0yhxknTK76/Kef38/8q++oX0FmTSRTweHhuDaeNl88r6bweGTwRYeHtYVMBYfZeNt88TwmT+qo+vxHBl/0cF3LPAeHWQVXfWIWnROrDw8P17Wsc3CYVdDTlWPl+bXtS/7cK8MOD8usTAWHO8etXnq6cuSmdNb0Hm8MZVmVqeBw57jV07IFM2tqsirY8ther7BrmZKp4DCrp0KTVW5KJ9WvbvUGh4dlhZccMTsM86++g0cGX6z6/BOPmcTdl8yvY0VmY+clR8waYPPF83hi1Tkce2R1TVhPP7/ffR7W8jIVHO4ct7Tcf/nCqs8t9Hl4jStrVZkKDneOW5qeWHUOE2ro/FizdTenrXC/h7WeTAWHWdoeXXlOTXt7vBp4prm1HAeH2Ti7smdWTavrvjwc9G4fqGNFZuPLwWFWB2uXzKkpPJau2+E7D2sZDg6zOlm7ZA5PrDqHE4+ZVNX5Lw+H53pYS3BwmNXZ3ZfMr+nuwyOurNk5OMwaYO2SOVXP9YD8iCsvkmjNysFh1iD3X76w6v3MIb9Iovs9rBllKjg8AdCa3cNXnV1TeLw8HN7P3JpOpoLDEwCtFTx81dk1nf/08/vd52FNJVPBYdYqal1dd83W3V7jypqGg8MsBSfVuCkU5Ne4cp+HNQMHh1kKxropVGGuh/s9LE0ODrMUjNwUKjelk2sWz656suDTz+/3cF1LzYS0CzBrVz1dOXq6coccu7R3J2u27q74/udeGaZ3+8Ah1zCrN99xmDWZWhZJXLpuhzvNreEcHGZNaO2SOVXP99jy2F6HhzWUg8OsST181dnMOGFyVedueWwv86++o74FmSUcHGZNrLCneTUeGXzR4WEN0RLBIWmypHskLUq7FrM0VDth0OFhjVDX4JB0g6RBSQ+MOL5Q0i5Jj0paXsWl/hS4pT5VmjW/X646x+FhTUMRUb+LS78HvAB8LyLekxzrAP4ZmA/0A9uAzwAdwMoRl/gCcDpwPHAU8JuI+FGlz+3u7o6+vr7x+hpmTeWz37qLLY/trercozpU89pY1r4k3RMR3ZXOq+sdR0TcCYz8E/5+4NGIeDwi9gM3A+dFxM6IWDTiMQh8GDgL+ENgiaSSNUu6UFKfpL49e/bU8VuZpWvtkjlVd5q/PBxepsTGXRp9HDngyaLn/cmxkiLikohYCnwf+FZEvDbKeddHRHdEdE+dOnVcCzZrJr3bB3hpf8m/BiW9PBxeXdfGVRozx0s11VZsL4uIGyteWDoXOPe0004bQ1lmza93+wAr1u9k6MBwTe8rzES/smdWPcqyNpPGHUc/cErR85OBp8bjwt6Pw7Ju9aZdNYdGwZqtu5m2fKPXuLLDlkZwbANmSJouaRLwaWBDCnWYtZyn9g2VPC6oeqb5c68MOzzssNR7OO5NwF3ATEn9ki6IiFeBi4BNwEPALRHx4Dh9nreOtUwbbR+Pk6Z01rQt7XOvjO2uxQzqPBw3LR6Oa1lVqo+jc2IHK8+f9foqudWurgtw7JEd3H/5wrrUaq2nKYbjNprvOCzrSu3jURwakO8A/9xZp9KhyncfbraysfAdh1kL690+wOpNu3hq3xAnTelk2YKZr4dI7/YBlq7bUdV1ql0Py7Kt2jsOb+Rk1qJGNlsN7Btixfo35msU/17JtOUbAZhxwmQ2XzxvXOu07MlUU5VZOyk1NHfowDCrN+0a87Bdr3Nl1cjUHYcnAFo7GW1o7mjHq/XI4IuH9X7LvkzdcXgCoLWTckNzR3vNbDxk6o7DrJ0sWzCz5NDcZQtmAlQctlvo1yil+LUTj5nE3ZfMH+/yrYVl6o7Dw3GtnZQbmlvNsN1qV9h9+vn9nHnV5vp8CWtJHo5r1sbmX31H1X0aHrKbfR6Oa2YVjRx6W675yqzAwWFmVSkOFc/3aG+Z6uMws8Nz4jGTqjrP8z3aW6aCw53jZofn7kvm1xQe1p7cOW5mZZXr93CHeba05eq4ZmZWfw4OMytrtPke1c4DsexxcJhZWZsvnndISBSPqurdPsDcVbczfflG5q66nd7tAylUaY3k4bhmVtFoQ2/LLe1ePEvdsiVTdxweVWXWWOWWdrfsylRweHVcs8aq19Lu1twyFRxm1ljllna37HJwmNmYLVswk86JHQcdK17a3bLJneNmNmaFDvDVm3bx1L4hTprSybIFM90xnnEODjM7LIX9P6x9uKnKzMxq4jsOMxs3vdsHRm22OvOqzTz9/P7Xz/WWtK0rU3ccnsdhlp7CZMCBfUMEb0wG7N0+cEhogLekbWWZCg7P4zBLT7nJgCNDo2C049bcMhUcZpYeTwZsHw4OMxsXngzYPhwcZjYuyk0GHG1XwWp3G7Tm4uAws3HR05Vj5fmzyE3pREBuSicrz59FT1eu5Ja0HlXVurx1rJmZAd461szM6sTBYWZmNXFwmJlZTRwcZmZWk6YPDknzJP1M0nWS5qVdj5lZu6trcEi6QdKgpAdGHF8oaZekRyUtr3CZAF4AjgL661WrmZlVp96r494IXAt8r3BAUgfwDWA++SDYJmkD0AGsHPH+LwA/i4h/lHQicDXw2TrXbGZNpHjF3ZGTB449soP7L1+YSl3trK53HBFxJ7B3xOH3A49GxOMRsR+4GTgvInZGxKIRj8GIeC153zPAkaN9lqQLJfVJ6tuzZ09dvo+ZNdbIFXdHeu6VYU6/7KcNr6vdpdHHkQOeLHrenxwrSdL5kr4J/C/ydy8lRcT1EdEdEd1Tp04dt2LNLD2lVtwd6blXyr9u4y+NjZxU4tio09cjYj2wvn7lmFmz8sq6zSmNO45+4JSi5ycDT43Hhb2Rk1m2eGXd5pRGcGwDZkiaLmkS8Glgw3hc2Bs5mWVLqRV3Rzr2yPKv2/ir93Dcm4C7gJmS+iVdEBGvAhcBm4CHgFsi4sFx+jzfcZhlyMgVd0fyqKp0eHVcMzMDvDqumZnVSaaCw01VZmb1l6ngcOe4mVn9ZSo4zMys/jIVHG6qMjOrv0yOqpK0B/hV2nWUcTzwm7SLOEyt/h1avX5o/e/Q6vVD9r7Db0dExTWbMhkczU5SXzVD3ppZq3+HVq8fWv87tHr90L7fIVNNVWZmVn8ODjMzq4mDIx3Xp13AOGj179Dq9UPrf4dWrx/a9Du4j8PMzGriOw4zM6uJg8PMzGri4GgwSR2Stkv6Udq1jIWkKZJulfSwpIckzUm7plpJ+oqkByU9IOkmSUelXVMlkm6QNCjpgaJjb5G0WdIjyc/j0qyxnFHqX538Obpf0v+RNCXNGisp9R2KXvsTSSHp+DRqq8Zo9Uv6sqRdyd+Jv6zmWg6OxvvP5PchaVV/Dfw0It4BnEGLfRdJOeCPgO6IeA/QQX4zsWZ3IzBy44nlwD9ExAzgH5LnzepGDq1/M/CeiDgd+GdgRaOLqtGNHPodkHQKMB/Y3eiCanQjI+qX9GHgPOD0iHg38N+ruZCDo4EknQycA3w77VrGQtKxwO8BfwsQEfsjYl+6VY3JBKBT0gTgaMZp6+J6iog7gb0jDp8HfDf5/btAT0OLqkGp+iPitmRjN4Ct5LeRblqj/H8A8DXgvwBNPdJolPq/BKyKiFeScwaruZaDo7GuIf8H7LW0CxmjtwJ7gO8kzW3fljQ57aJqERED5P9VtRv4NfBsRNyWblVjdmJE/Bog+XlCyvUcji8AP0m7iFpJ+jgwEBH3pV3LGL0d+KCkuyX9o6T3VfMmB0eDSFoEDEbEPWnXchgmAO8F/mdEdAEv0tzNI4dI+gHOA6YDJwGTJX0u3aram6RLgFeBtWnXUgtJRwOXAH+Wdi2HYQJwHHAWsAy4RVKpXXoP4uBonLnAxyU9AdwMfETSmnRLqlk/0B8RdyfPbyUfJK3ko8AvI2JPRBwA1gMfSLmmsXpa0r8GSH5W1czQTCR9HlgEfDZab1LZ28j/A+S+5O/1ycC9kn4r1apq0w+sj7xfkG8NqdjB7+BokIhYEREnR8Q08p2xt0dES/1LNyL+BXhS0szk0O8D/5RiSWOxGzhL0tHJv6x+nxbr4C+yAfh88vvngb9LsZaaSVoI/Cnw8Yh4Ke16ahUROyPihIiYlvy97gfem/w9aRW9wEcAJL0dmEQVq/06OKxWXwbWSrofmA38Rcr11CS5W7oVuBfYSf7vQNMvGyHpJuAuYKakfkkXAKuA+ZIeIT+qZ1WaNZYzSv3XAscAmyXtkHRdqkVWMMp3aBmj1H8D8NZkiO7NwOerufPzkiNmZlYT33GYmVlNHBxmZlYTB4eZmdXEwWFmZjVxcJiZWU0cHNbSJA0nQzkLj+XJ8Q8mq33ukNSZrMT6oKTVY/iM/zri+c/HufYHJd0n6WJJRySvdUv6epn3TpP0h+NRh1mtPBzXWpqkFyLiTSWOXwfcHRHfSZ4/B0wtLOY2Hp9xuIqvK+kE4PvAloi4rIr3zgP+JCIWjXddZpX4jsMyR9J/BD4F/JmktZI2AJOBuyUtljRV0v+WtC15zE3e9yZJ35G0M9kj4g8krSK/ku4OSWuT815Ifq6TdHbR596YvKcjucPZllznP1WqOVmV9ELgIuXNU7Jni6QPFd1RbZd0DPnJfh9Mjn0luQP5maR7k8cHkvfOk3SH3thDZW1hLSJJ75P08+Ru5xeSjhlL7daGIsIPP1r2AQwDO4oei5PjNwKfLDrvhaLfvw/8bvL7qcBDye9fBa4pOu+4ke8tfg58Avhu8vsk4Emgk3wAXJocPxLoA6aXqP2FEseeAU4E5gE/So79EJib/P4m8gvTvf56cvxo4Kjk9xlAX/L7POBZ8usoHUF+5vDvJvU+DrwvOe/Y5LpV1e5Hez8m1JgzZs1mKCJm1/iejwLvKloE9NjkX/EfpWhTp4h4psJ1fgJ8XdKR5DfIuTMihiR9DDhd0ieT895M/j/mv6yitlIrk24Brk7ueNZHRL8OXcB0InCtpNnkw/TtRa/9IiL6ASTtAKaRD5NfR8S25Ls+l7x+OLVbm3BwWDs6ApgTEUPFB5MmnKo7/SLiZUl3AAuAxcBNhUsBX46ITbUUJemt5P+jPwi8s+hzVknaCJwNbJX00RJv/wrwNPldGY8AXi56rbhfZ5j83/vRvuuYarf24j4Oa0e3ARcVniT/Si91vLCH9wFJE0e51s3AfwA+CBT+Y7sJ+FLhPZLergobXkmaClwHXBsRMeK1t0V+Jdavkm86egfwPPkFAgveTP4O4jXg35HfErech4GTlGzck/RvTBhL7dZ+HBzW6god14VHNSvE/hHQnXT+/hPwxeT4lcBxkh6QdB/w4eT49cD9hc7xEW4jv53u30fE/uTYt8kvN3+v8quOfpPSd/eF2h8E/j651uUlzltaVNMQ+Say+4FXk47trwD/A/i8pK3km6leLPc/QFLrYuBvkutuBo6qoXZrYx6Oa2ZmNfEdh5mZ1cTBYWZmNXFwmJlZTRwcZmZWEweHmZnVxMFhZmY1cXCYmVlN/j/URiqmj9wvEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(eff_dist[rowid,:],pij[rowid,:],'o')\n",
    "plt.xlabel('Effective Distance')\n",
    "plt.ylabel('pij')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 与流量排序做比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 孝感市 13 2.980478616994291\n",
      "1 黄冈市 10 3.037374727396292\n",
      "2 荆州市 12 3.7266514129156585\n",
      "3 咸宁市 15 3.9941694566455666\n",
      "4 鄂州市 14 4.227351434727007\n",
      "5 襄阳市 15 4.237179631878021\n",
      "6 黄石市 15 4.2789522017520465\n",
      "7 荆门市 11 4.412218955904846\n",
      "8 随州市 14 4.438999246796143\n",
      "9 仙桃市 13 4.516069097497828\n",
      "10 宜昌市 13 4.573724208503557\n",
      "11 天门市 14 4.870426215772632\n",
      "12 十堰市 13 4.9832323597688095\n",
      "13 信阳市 13 5.208159052890949\n",
      "14 重庆市 11 5.367613802259018\n",
      "15 潜江市 16 5.473031232823582\n",
      "16 长沙市 10 5.587191788867874\n",
      "17 北京市 10 5.751427751255027\n",
      "18 南阳市 14 5.974024661804292\n",
      "19 上海市 10 6.016305320813929\n",
      "20 驻马店市 14 6.026172347218409\n",
      "21 郑州市 11 6.137315133661776\n",
      "22 九江市 13 6.256416329600447\n",
      "23 岳阳市 13 6.259292709764764\n",
      "24 深圳市 9 6.301076903215052\n",
      "25 广州市 11 6.30256274134972\n",
      "26 南昌市 12 6.348199251019906\n",
      "27 成都市 10 6.373804595803513\n",
      "28 佛山市 11 6.384934319113159\n",
      "29 安庆市 13 6.394024560113519\n",
      "30 周口市 13 6.425457483154485\n",
      "31 合肥市 11 6.530407853420411\n",
      "32 阜阳市 13 6.66392870136436\n",
      "33 西安市 13 6.6940627053281245\n",
      "34 常德市 14 6.700480538613006\n",
      "35 商丘市 16 6.727501410718335\n",
      "36 无锡市 13 6.733241013381375\n",
      "37 金华市 13 6.842440305346367\n",
      "38 青岛市 10 6.912120225984356\n",
      "39 中山市 13 6.9399965955126115\n",
      "40 台州市 11 7.009494390009533\n",
      "41 衡阳市 14 7.030037124252194\n",
      "42 南京市 13 7.0362446213511936\n",
      "43 常州市 14 7.084185282611123\n",
      "44 嘉兴市 13 7.100646559665194\n",
      "45 宜春市 14 7.124389570298788\n",
      "46 昆明市 10 7.176576106857657\n",
      "47 杭州市 11 7.199488712879078\n",
      "48 济南市 14 7.262433514017441\n",
      "49 福州市 12 7.301551606081853\n",
      "50 绍兴市 13 7.354334876301262\n",
      "51 温州市 11 7.361390936019055\n",
      "52 泉州市 13 7.372987499903893\n",
      "53 廊坊市 16 7.455544778232776\n",
      "54 兰州市 13 7.467449680739094\n",
      "55 乌鲁木齐市 13 7.473455704799306\n",
      "56 南通市 13 7.473455704799306\n",
      "57 沈阳市 12 7.4855770653316505\n",
      "58 海口市 12 7.510269677922023\n",
      "59 六安市 13 7.573633368139551\n",
      "60 大连市 12 7.601836871447513\n",
      "61 保定市 14 7.601836871447513\n",
      "62 桂林市 13 7.608709750735275\n",
      "63 太原市 12 7.608709750735275\n",
      "64 苏州市 13 7.673459264556343\n",
      "65 娄底市 13 7.701023882683792\n",
      "66 烟台市 14 7.725631088794177\n",
      "67 邵阳市 14 7.752602956933459\n",
      "68 平顶山市 14 7.773259137783432\n",
      "69 临沂市 13 7.7814224484225925\n",
      "70 运城市 14 7.789652947559108\n",
      "71 长春市 13 7.797951750373803\n",
      "72 南宁市 15 7.804657369526028\n",
      "73 益阳市 14 7.809069876281195\n",
      "74 洛阳市 12 7.814446288628351\n",
      "75 柳州市 12 7.840511364792599\n",
      "76 株洲市 13 7.845527988378772\n",
      "77 泰州市 15 7.858055674443508\n",
      "78 安阳市 15 7.858055674443508\n",
      "79 抚州市 11 7.875913291843515\n",
      "80 潍坊市 13 7.884963127363433\n",
      "81 漯河市 15 7.897741242905977\n",
      "82 银川市 12 7.912614658693943\n",
      "83 湖州市 15 7.931483142998326\n",
      "84 沧州市 13 7.950714504926213\n",
      "85 济宁市 14 7.980273307167757\n",
      "86 西宁市 15 8.010732514652467\n",
      "87 三亚市 12 8.031566601555308\n",
      "88 湘潭市 13 8.031566601555308\n",
      "89 芜湖市 14 8.042148710885845\n",
      "90 汕头市 14 8.052844000002594\n",
      "91 咸阳市 13 8.06365491610681\n",
      "92 湛江市 10 8.074583986638999\n",
      "93 镇江市 16 8.108106678677643\n",
      "94 扬州市 12 8.108106678677643\n",
      "95 淄博市 15 8.131096196902341\n",
      "96 天津市 9 8.149182898259149\n",
      "97 石家庄市 12 8.153189663360571\n",
      "98 盐城市 14 8.154626694312537\n",
      "99 漳州市 15 8.166602885359252\n",
      "100 怀化市 12 8.166602885359252\n",
      "101 包头市 16 8.178724245891596\n",
      "102 铜仁市 12 8.19099433848341\n",
      "103 连云港市 13 8.203416858481969\n",
      "104 南平市 16 8.203416858481969\n",
      "105 吉安市 13 8.215995640688828\n",
      "106 邢台市 16 8.215995640688828\n",
      "107 郴州市 13 8.215995640688828\n",
      "108 莆田市 16 8.228734666466258\n",
      "109 永州市 13 8.228734666466258\n",
      "110 宁德市 13 8.228734666466258\n",
      "111 渭南市 17 8.254710152869517\n",
      "112 亳州市 13 8.26795537961954\n",
      "113 临汾市 16 8.26795537961954\n",
      "114 淮安市 15 8.281378399951679\n",
      "115 长治市 14 8.281378399951679\n",
      "116 绵阳市 12 8.281378399951679\n",
      "117 晋中市 15 8.308777374139794\n",
      "118 上饶市 14 8.333072136002563\n",
      "119 玉林市 14 8.33694825110649\n",
      "120 宿迁市 14 8.33694825110649\n",
      "121 达州市 13 8.33694825110649\n",
      "122 威海市 12 8.35133698855859\n",
      "123 贵阳市 12 8.356565972504992\n",
      "124 马鞍山市 14 8.365935787979742\n",
      "125 防城港市 14 8.365935787979742\n",
      "126 许昌市 16 8.401232822325579\n",
      "127 景德镇市 14 8.426560409796178\n",
      "128 曲靖市 15 8.458309108110758\n",
      "129 蚌埠市 13 8.474569628982538\n",
      "130 丽水市 15 8.474569628982538\n",
      "131 三门峡市 12 8.491098930933749\n",
      "132 韶关市 11 8.491098930933749\n",
      "133 肇庆市 13 8.50790604925013\n",
      "134 汉中市 16 8.50790604925013\n",
      "135 北海市 12 8.542392225321299\n",
      "136 清远市 13 8.542392225321299\n",
      "137 安康市 14 8.550880016188774\n",
      "138 滁州市 13 8.578110307923378\n",
      "139 大同市 14 8.596459446591574\n",
      "140 三明市 15 8.596459446591574\n",
      "141 舟山市 11 8.634199774574423\n",
      "142 萍乡市 11 8.634199774574423\n",
      "143 赤峰市 15 8.634199774574423\n",
      "144 聊城市 14 8.634199774574423\n",
      "145 广安市 11 8.634950968785443\n",
      "146 六盘水市 16 8.653617860431524\n",
      "147 南充市 15 8.653617860431524\n",
      "148 揭阳市 15 8.673420487727704\n",
      "149 吕梁市 16 8.673420487727704\n",
      "150 宣城市 15 8.693623195045223\n",
      "151 日照市 13 8.693623195045223\n",
      "152 德州市 14 8.756802096666753\n",
      "153 宜宾市 16 8.778781003385529\n",
      "154 衢州市 13 8.778781003385529\n",
      "155 鄂尔多斯市 15 8.801253859237587\n",
      "156 淮南市 15 8.801253859237587\n",
      "157 新乡市 14 8.818008461618007\n",
      "158 枣庄市 17 8.824243377462286\n",
      "159 呼伦贝尔市 13 8.824243377462286\n",
      "160 淮北市 15 8.84777387487248\n",
      "161 白银市 13 8.84777387487248\n",
      "162 遂宁市 14 8.84777387487248\n",
      "163 德阳市 14 8.871871426451541\n",
      "164 滨州市 15 8.896564039041913\n",
      "165 新余市 13 8.896564039041913\n",
      "166 厦门市 13 8.92842014009073\n",
      "167 池州市 13 8.947857333429464\n",
      "168 泸州市 14 8.947857333429464\n",
      "169 梧州市 11 8.947857333429464\n",
      "170 昭通市 16 8.974525580511624\n",
      "171 丽江市 14 8.974525580511624\n",
      "172 东莞市 14 8.997721603495553\n",
      "173 河池市 13 9.00192455469974\n",
      "174 佳木斯市 16 9.030095431666435\n",
      "175 大庆市 13 9.059082968539688\n",
      "176 齐齐哈尔市 15 9.059082968539688\n",
      "177 承德市 14 9.059082968539688\n",
      "178 眉山市 15 9.059082968539688\n",
      "179 汕尾市 16 9.088935931689369\n",
      "180 吉林市 13 9.088935931689369\n",
      "181 乐山市 15 9.088935931689369\n",
      "182 铜陵市 14 9.119707590356123\n",
      "183 黄山市 15 9.119707590356123\n",
      "184 百色市 13 9.184246111493694\n",
      "185 内江市 14 9.184246111493694\n",
      "186 朔州市 16 9.253238982980646\n",
      "187 哈尔滨市 13 9.28365616148636\n",
      "188 鹤壁市 16 9.289606627151521\n",
      "189 玉溪市 15 9.289606627151521\n",
      "190 延安市 14 9.289606627151521\n",
      "191 阳江市 13 9.289606627151521\n",
      "192 铁岭市 14 9.327346955134367\n",
      "193 葫芦岛市 14 9.327346955134367\n",
      "194 邯郸市 15 9.35178393337296\n",
      "195 张掖市 16 9.407389662807903\n",
      "196 阳泉市 15 9.407389662807903\n",
      "197 自贡市 14 9.407389662807903\n",
      "198 开封市 16 9.416121697356623\n",
      "199 鹰潭市 17 9.4499492772267\n",
      "200 通辽市 16 9.494401039797534\n",
      "201 四平市 16 9.494401039797534\n",
      "202 中卫市 13 9.494401039797534\n",
      "203 商洛市 15 9.494401039797534\n",
      "204 儋州市 15 9.494401039797534\n",
      "205 梅州市 16 9.494401039797534\n",
      "206 定西市 15 9.540921055432426\n",
      "207 资阳市 16 9.540921055432426\n",
      "208 牡丹江市 11 9.589711219601858\n",
      "209 固原市 14 9.589711219601858\n",
      "210 锡林郭勒盟 14 9.641004513989408\n",
      "211 丹东市 16 9.695071735259685\n",
      "212 绥化市 13 9.695071735259685\n",
      "213 陇南市 16 9.752230149099633\n",
      "214 盘锦市 16 9.752230149099633\n",
      "215 临高县 12 9.812854770916069\n",
      "216 兴安盟 16 9.812854770916069\n",
      "217 赣州市 14 9.87048357788233\n",
      "218 营口市 15 9.87739329205364\n",
      "219 朝阳市 13 9.87739329205364\n",
      "220 宁波市 12 9.9383391409131\n",
      "221 松原市 13 9.946386163540591\n",
      "222 惠州市 10 10.01080827596614\n",
      "223 双鸭山市 16 10.020494135694312\n",
      "224 本溪市 14 10.100536843367848\n",
      "225 澄迈县 15 10.100536843367848\n",
      "226 珠海市 10 10.122315420118559\n",
      "227 雅安市 14 10.282858400161803\n",
      "228 保山市 16 10.282858400161803\n",
      "229 七台河市 16 10.282858400161803\n",
      "230 万宁市 13 10.38821891581963\n",
      "231 菏泽市 15 10.405762501373593\n",
      "232 徐州市 16 10.516100056779056\n",
      "233 铜川市 15 10.793684023927794\n",
      "234 武汉市 0 inf\n"
     ]
    }
   ],
   "source": [
    "for i,name in enumerate(city_names_dis):\n",
    "    print(i,name,firsts[i],firsts_dis[i])\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 有效距离和首感日期的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.343289817860591 0.4305486525111803\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "yy=np.array(firsts)*1.0\n",
    "xx=np.array(firsts_dis)\n",
    "bools=(xx!=inf)#&(xx>5.5)\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(xx[bools],yy[bools])\n",
    "#slope1, intercept1, _,_,_ = stats.linregress(yy[bools],xx[bools])\n",
    "bools1 = (xx!=inf)&(xx>5.5)\n",
    "slope1, intercept1, r_value2,_,_ = stats.linregress(xx[bools1],yy[bools1])\n",
    "print(r_value,r_value2)\n",
    "\n",
    "bools2 = (sorted_distance!=inf)&(sorted_distance>=11)\n",
    "plt.plot(sorted_distance[bools2], slope1 * sorted_distance[bools2] + intercept1, 'ko',alpha=0.5)\n",
    "plt.plot(firsts_dis,firsts,'o')\n",
    "plt.plot(xx[bools1],yy[bools1],'o')\n",
    "\n",
    "plt.plot(xx[bools],slope*xx[bools]+intercept,'r-',label='{:2.2f}x+{:2.2f}'.format(slope,intercept))\n",
    "#plt.plot(xx[bools],(1/slope1)*xx[bools]-intercept1/slope1,'-',label=\n",
    "#         '颠倒x、y轴拟合：{:2.2f}x{:2.2f}'.format(1/slope1,-intercept1/slope1))\n",
    "plt.plot(xx[bools],slope1*xx[bools]+intercept1,'-',\n",
    "         label='去掉湖北省内的城市：{:2.2f}x+{:2.2f}'.format(slope1,intercept1))\n",
    "\n",
    "zhfont1 = matplotlib.font_manager.FontProperties(fname='/Users/zhangjiang/Library/Fonts/SimHei.ttf', size=10)\n",
    "show_cities = sorted_cities[bools2]\n",
    "for n,i in enumerate(show_cities):\n",
    "    xcor =sorted_distance[bools2][n]\n",
    "    ycor = slope1 * xcor + intercept1\n",
    "    #print(xcor, ycor)\n",
    "    city_name = find_in_nodes(i, nodes)\n",
    "    plt.text(xcor,ycor,city_name,fontproperties=zhfont1,alpha=0.5)\n",
    "for i in range(13):\n",
    "    plt.text(firsts_dis[i],firsts[i],city_names_dis[i],fontproperties=zhfont1)\n",
    "plt.text(firsts_dis[17],firsts[17],city_names_dis[17],fontproperties=zhfont1)\n",
    "plt.text(firsts_dis[19],firsts[19],city_names_dis[19],fontproperties=zhfont1)\n",
    "plt.text(firsts_dis[24],firsts[24],city_names_dis[24],fontproperties=zhfont1)\n",
    "plt.text(firsts_dis[25],firsts[25],city_names_dis[25],fontproperties=zhfont1)\n",
    "\n",
    "plt.xlabel('Effective Distance',fontsize=14)\n",
    "plt.ylabel('Reported Date',fontsize=14)\n",
    "#plt.ylim([15, 50])\n",
    "plt.legend(loc='upper left', shadow=True, numpoints = 1,fontsize=10,prop = zhfont1 )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 传播模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "传播模型如下：\n",
    "\n",
    "\n",
    "$\\partial j_n/\\partial t = \\alpha s_n j_n - \\beta j_n + \\gamma \\sum_{m\\neq n}P_{mn}(j_m-j_n)$\n",
    "\n",
    "\n",
    "\n",
    "$\\partial s_n/\\partial t = -\\alpha s_n j_n + \\gamma \\sum_{m\\neq n}P_{mn}(s_m-s_n)$\n",
    "\n",
    "\n",
    "$r_n = 1 - s_n - j_n$\n",
    "\n",
    "其中$j_n$是n城市感染比例；$s_n$是n城市疑似人群比例；$\\alpha$为感染系数；$\\beta$为康复系数；$\\epsilon$为平均每城市人口数；$\\gamma$为平均流动人口占总人口的比例；$P_{mn}$为从n城市到m城市的n城市流动人口占比。\n",
    "\n",
    "在数值求解中，$\\sum_{m\\neq n}P_{mn}(j_m-j_n)$的计算用了矩阵和向量的乘法，并做了简化，即$\\sum_{m\\neq n}P_{mn}(j_m-j_n)=\\sum_m P_{mn}j_m-j_n$，写成矩阵方程为：$j\\cdot P - j$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.integrate import odeint\n",
    "\n",
    "def protect_decay(t, t0, eta, rate_time):\n",
    "    epsilon = 0.001\n",
    "    r = 2 * np.log((1-epsilon)/epsilon) / rate_time\n",
    "    x0 = t0 + rate_time/2\n",
    "    decay = eta / (1 + np.exp(r * (t - x0))) + 1 - eta\n",
    "    return decay\n",
    "    \n",
    "def diff(sicol, t, alpha, beta, gamma, eta, rate_time, protect_day, pijt, intervention):\n",
    "    sz = sicol.shape[0] // 2\n",
    "    js = sicol[:sz]\n",
    "    ss = sicol[sz:]\n",
    "    jterm = js.dot(pijt) - js\n",
    "    sterm = ss.dot(pijt) - ss\n",
    "    if intervention:\n",
    "        cross_term = alpha * protect_decay(t, protect_day, eta, rate_time) * js * ss\n",
    "    else:\n",
    "        cross_term = alpha  * js * ss\n",
    "    delta_i = cross_term - beta * js + gamma * jterm\n",
    "    delta_s = - cross_term + gamma * sterm\n",
    "    output = np.r_[delta_i, delta_s]\n",
    "    return output\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 跑一下正式数据\n",
    "\n",
    "其中各个参数取值按照Science论文中的数值，可以考虑带代入我国参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 计算流量比例数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1255\n",
      "0.736842105263158\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timespan = np.linspace(0, 200, 1000)\n",
    "js0 = np.zeros(len(nodes))\n",
    "ss0 = np.ones(len(nodes))\n",
    "js0[nodes['武汉市']] = float(first_cases)/float(city_properties['武汉市']['pop'])#1e-4\n",
    "r0=3.8\n",
    "beta = 1/10 #一个病患从确诊到治愈（死亡）假设平均需要10天\n",
    "alpha = r0 * beta\n",
    "\n",
    "gamma = flowing_ratio\n",
    "print(gamma)\n",
    "\n",
    "eta = (1-1.0/r0)\n",
    "print(eta)\n",
    "rate_time = 30\n",
    "#武汉封城1月23日，数据起始日：1月10日\n",
    "result = odeint(diff, np.r_[js0,ss0], timespan, args = (alpha, beta, gamma, eta, \n",
    "                                                        rate_time, 13, np.transpose(pij),False))\n",
    "\n",
    "cities = ['武汉市','孝感市','黄冈市','深圳市','荆州市','襄阳市','上海市','北京市','黄石市','广州市']\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "plot_time_span = 700\n",
    "for i in range(result.shape[1]//2):\n",
    "    for k,v in nodes.items():\n",
    "        if v == i:\n",
    "            cityname = k\n",
    "    plt.plot(timespan[:plot_time_span],result[:plot_time_span,i]*city_properties[cityname]['pop']\n",
    "             ,alpha=0.1)\n",
    "colors = plt.cm.jet(np.linspace(0,1,len(cities)))\n",
    "for n,i in enumerate(cities):\n",
    "    cityname = i\n",
    "    if cityname[-1]=='市':\n",
    "        cityname1 = cityname[:-1]\n",
    "    itm = all_cases.get(cityname1,[])\n",
    "    if len(itm)==0:\n",
    "        itm = all_cases.get(cityname,[])\n",
    "    if len(itm)>0:\n",
    "        idx = nodes.get(cityname,-1)\n",
    "        if idx>=0:  \n",
    "            plt.plot(timespan[:plot_time_span],result[:plot_time_span,idx]*city_properties[cityname]['pop']\n",
    "                     ,color=colors[n],label=cityname,linewidth=2)\n",
    "            infected_number = itm[1] - itm[2] - itm[3]\n",
    "            plt.plot(itm[0], infected_number, 'o',color=colors[n])\n",
    "\n",
    "zhfont1 = matplotlib.font_manager.FontProperties(fname='/Users/zhangjiang/Library/Fonts/SimHei.ttf', size=16)\n",
    "plt.legend(loc='upper left', shadow=True, numpoints = 1,fontsize=10,prop = zhfont1 )\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Population')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_pop = np.zeros([result.shape[0]])\n",
    "all_recovery = np.zeros([result.shape[0]])\n",
    "for k,city in city_properties.items():\n",
    "    idx = nodes[k]\n",
    "    all_pop += result[:, idx] * city['pop']\n",
    "    all_recovery += (1-result[:, idx]-result[:, len(nodes)+idx]) * city['pop']\n",
    "plt.figure(figsize = (15,8))\n",
    "country_pop = np.sum([city['pop'] for k,city in city_properties.items()])\n",
    "plot_time_span = 700\n",
    "plt.plot(timespan[:plot_time_span],all_pop[:plot_time_span],label='感染人数')\n",
    "#plt.plot(timespan[:plot_time_span],all_recovery[:plot_time_span]*death_ratio, label='累积死亡人数')\n",
    "plt.plot(timespan[:plot_time_span],all_recovery[:plot_time_span]*(1-death_ratio), label = '累积康复人数')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Population')\n",
    "plt.legend(loc='upper left', shadow=True, numpoints = 1,fontsize=10,prop = zhfont1 )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建模干预的情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "建模思路是将原方程改为：\n",
    "\n",
    "$\\partial j_n/\\partial t = \\alpha \\xi(t,t_0,t_m,\\eta) s_n j_n - \\beta j_n + \\gamma \\sum_{m\\neq n}P_{mn}(j_m-j_n)$\n",
    "\n",
    "\n",
    "\n",
    "$\\partial s_n/\\partial t = -\\alpha \\xi(t,t_0,t_m,\\eta) s_n j_n + \\gamma \\sum_{m\\neq n}P_{mn}(s_m-s_n)$\n",
    "\n",
    "\n",
    "$r_n = 1 - s_n - j_n$\n",
    "\n",
    "其中，\n",
    "\n",
    "$\\xi(t,t_0,t_m,\\eta) = \\frac{\\eta}{1 + \\exp(\\lambda (t - t_0 - t_m/2))} + 1 - \\eta$\n",
    "\n",
    "这里，\n",
    "\n",
    "$\\lambda = 2 \\frac{\\log(\\frac{1-\\epsilon}{\\epsilon})}{t_m}, \\epsilon = 0.01$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1255\n",
      "0.8947368421052632\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#有干预情况：\n",
    "\n",
    "timespan = np.linspace(0, 200, 1000)\n",
    "js0 = np.zeros(len(nodes))\n",
    "ss0 = np.ones(len(nodes))\n",
    "js0[nodes['武汉市']] = float(first_cases)/float(city_properties['武汉市']['pop'])#1e-4\n",
    "r0=3.8\n",
    "beta = 1/10 #一个病患从确诊到治愈（死亡）假设平均需要10天\n",
    "alpha = r0 * beta\n",
    "\n",
    "gamma = flowing_ratio\n",
    "print(gamma)\n",
    "\n",
    "eta = 1-4.0*beta/r0\n",
    "print(eta)\n",
    "rate_time = 30\n",
    "#武汉封城1月23日，数据起始日：1月10日\n",
    "result = odeint(diff, np.r_[js0,ss0], timespan, args = (alpha, beta, gamma, eta, \n",
    "                                                        rate_time, 13, np.transpose(pij),True))\n",
    "\n",
    "cities = ['武汉市','孝感市','黄冈市','深圳市','荆州市','襄阳市','上海市','北京市','黄石市','广州市']\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "plot_time_span = 1000\n",
    "for i in range(result.shape[1]//2):\n",
    "    for k,v in nodes.items():\n",
    "        if v == i:\n",
    "            cityname = k\n",
    "    plt.plot(timespan[:plot_time_span],result[:plot_time_span,i]*city_properties[cityname]['pop']\n",
    "             ,alpha=0.1)\n",
    "colors = plt.cm.jet(np.linspace(0,1,len(cities)))\n",
    "for n,i in enumerate(cities):\n",
    "    cityname = i\n",
    "    if cityname[-1]=='市':\n",
    "        cityname1 = cityname[:-1]\n",
    "    itm = all_cases.get(cityname1,[])\n",
    "    if len(itm)==0:\n",
    "        itm = all_cases.get(cityname,[])\n",
    "    if len(itm)>0:\n",
    "        idx = nodes.get(cityname,-1)\n",
    "        if idx>=0:  \n",
    "            plt.plot(timespan[:plot_time_span],result[:plot_time_span,idx]*city_properties[cityname]['pop']\n",
    "                     ,color=colors[n],label=cityname,linewidth=2)\n",
    "            infected_number = itm[1] - itm[2] - itm[3]\n",
    "            plt.plot(itm[0], infected_number, 'o',color=colors[n])\n",
    "\n",
    "zhfont1 = matplotlib.font_manager.FontProperties(fname='/Users/zhangjiang/Library/Fonts/SimHei.ttf', size=16)\n",
    "plt.legend(loc='upper right', shadow=True, numpoints = 1,fontsize=10,prop = zhfont1 )\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Population')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_pop = np.zeros([result.shape[0]])\n",
    "all_recovery = np.zeros([result.shape[0]])\n",
    "for k,city in city_properties.items():\n",
    "    idx = nodes[k]\n",
    "    all_pop += result[:, idx] * city['pop']\n",
    "    all_recovery += (1-result[:, idx]-result[:, len(nodes)+idx]) * city['pop']\n",
    "plt.figure(figsize = (15,8))\n",
    "country_pop = np.sum([city['pop'] for k,city in city_properties.items()])\n",
    "plot_time_span = 700\n",
    "death_ratio = 0.031\n",
    "plt.plot(timespan[:plot_time_span],all_pop[:plot_time_span],label='感染人数')\n",
    "plt.plot(timespan[:plot_time_span],all_recovery[:plot_time_span]*death_ratio, label='累积死亡人数')\n",
    "plt.plot(timespan[:plot_time_span],all_recovery[:plot_time_span]*(1-death_ratio), label = '累积康复人数')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Population')\n",
    "plt.legend(loc='upper left', shadow=True, numpoints = 1,fontsize=10,prop = zhfont1 )\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1c9e8ef0>]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(timespan[:plot_time_span],all_recovery[:plot_time_span]*death_ratio, label='累积死亡人数')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
